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Poznań University of Technology

Faculty of Electronics and Telecommunications

Jacek Góra

Radio Resource Management for

Multi-Carrier Relay-Enhanced Networks

Doctoral thesis

Supervisor:

prof. dr hab. inż. Krzysztof Wesołowski

Poznań, 2013

Politechnika Poznańska

Wydział Elektroniki i Telekomunikacji

Jacek Góra

Zarządzanie zasobami radiowymi

w sieciach wielopasmowych

wykorzystujących stacje przekaźnikowe

Rozprawa doktorska

Promotor:

prof. dr hab. inż. Krzysztof Wesołowski

Poznań, 2013

To my wife Ania and my son Tomek

i

Abstract

At the moment of writing this dissertation development of the fourth generation (4G) of

radiocommunication systems is already well established. The defined 4G systems

introduce a set of innovative concepts for cellular networks that enable performance

boost over earlier generation systems. Each of those concepts is a standalone feature that

can be implemented individually in a network to improve specific performance

parameters. However, as those 4G features were developed independently from each

other, possible interactions between them are not fully known. This dissertation makes a

step in this direction by presenting study on coexistence of two key 4G features: wireless

relaying and multi-carrier spectrum arrangement with carrier aggregation.

Wireless relaying is a technique of multi-hop transmission over radio interface

with support of relaying nodes (RNs). With the RNs introduced into a cellular system a

hierarchical heterogeneous network is formed, i.e. a relay-enhanced network (REN). An

REN is hierarchical as each RN is served from a superior (i.e. donor) node and may have

own subordinate RNs and user terminals attached. In this dissertation radio resource

management (RRM) relations between an RN, its donor and its subordinate nodes are

analysed. This leads to formulation of a general REN RRM framework. The REN RRM

framework is next enhanced with quality-of-service (QoS) awareness on the basis of

utility theory. In this context various traffic scenarios for RENs are considered to reflect

heterogeneity of today’s and future networks. The key performance indicators

considered for optimization are: resource utilization efficiency and end-user

performance fairness.

The second 4G feature treated in this dissertation is multi-carrier spectrum

arrangement with carrier aggregation. In the context of relaying various RN

configurations, single- and multi-carrier, are analysed and compared. Especially

application of the carrier aggregation concept to RNs is considered to eliminate

transmission bottlenecks on multi-hop links. The conducted analysis shows multiple

advantages of multi-carrier relaying operation schemes over baseline single-carrier ones.

This dissertation demonstrates also that in heterogeneous networks, such as

RENs, the multi-carrier spectrum arrangement has potential to increase system

performance beyond the scope of pure bandwidth extension. The additional benefits are

available by employing carrier-based coordination techniques. In this dissertation

proposals of such solutions for adaptive load balancing and interference coordination are

given and evaluated with the special focus on application to RENs.

Overall, this dissertation provides a complete RRM framework for RENs,

especially those operated with multi-carrier spectrum arrangement. The proposed

solutions and the conducted evaluations are anchored in the LTE-A system. The

underlying mechanisms are, however, generic and transferable to other 4G systems.

ii

iii

Streszczenie

W czasie pisania niniejszej rozprawy ukazały się pierwsze wersje standardów systemów

czwartej generacji (ang. 4th generation, 4G). Aby umożliwić znaczący wzrost

wydajności sieci radiokomunikacyjnych w stosunku do systemów wcześniejszych

generacji, w systemach 4G zaproponowano szereg innowacyjnych rozwiązań. Każda

z zaproponowanych funkcjonalności może być wdrożona oddzielnie w sieci

komórkowej, aby poprawić jej określone parametry. Jednakże, funkcjonowanie tychże

rozwiązań jednocześnie w jednej sieci nie było dotychczas szczegółowo badane.

W rezultacie możliwe interakcje pomiędzy nimi nie są w pełni znane. W niniejszej

rozprawie podjęto ten temat i zaprezentowano wyniki badań nad współistnieniem dwóch

kluczowych koncepcji 4G: transmisji wieloskokowych oraz agregacji pasm.

Koncepcja transmisji wieloskokowej polega na komunikacji z pośrednictwem

radiowych stacji przekaźnikowych (ang. relaying nodes, RNs). Zastosowanie RNs

prowadzi do powstania tzw. hierarchicznych heterogenicznych sieci komórkowych.

Hierarchicznych, ponieważ każda stacja przekaźnikowa podlega nadrzędnej stacji

dostępowej, a jednocześnie może obsługiwać inne stacje przekaźnikowe i terminale

użytkowników. W niniejszej pracy przeprowadzono analizę zależności istniejących

pomiędzy stacjami przekaźnikowymi, a ich stacjami nad- i podrzędnymi w zakresie

zarządzania zasobami radiowymi (ang. radio resource management, RRM). Na tej

podstawie zdefiniowano zbiór zasad RRM dla sieci komórkowych uzupełnionych

o radiowe stacje przekaźnikowe (ang. relay-enhanced networks, RENs). Następnie

koncepcję REN RRM poszerzono o mechanizmy związane z zapewnieniem parametrów

jakościowych transmisji (ang. qulity-of-service, QoS). W tym celu posłużono się

elementami teorii użyteczności. Funkcjonowanie zaproponowanych mechanizmów

RRM oceniono pod względem efektywności wykorzystania zasobów radiowych oraz

jednorodności parametrów jakościowych transmisji dostępnych dla użytkowników

obsługiwanych przez stacje różnego typu.

Drugą z koncepcji 4G rozważaną w tej rozprawie jest wielopasmowa organizacja

widma transmisyjnego z agregacją pasm. W odniesieniu do systemów wzbogaconych

o stacje przekaźnikowe przeanalizowano i porównano funkcjonowanie konfiguracji

jedno- i wielopasmowych. W szczególności wzięto pod uwagę możliwość agregacji

pasm dla stacji przekaźnikowych w celu eliminacji wąskich gardeł transmisji

wieloskokowych. Przeprowadzona analiza ukazała wiele korzyści wynikających

z realizacji transmisji wieloskokowych w systemach wielopasmowych w stosunku do

tradycyjnych konfiguracji jednopasmowych.

W dalszej kolejności w rozprawie pokazano, że wielopasmowa organizacja

widma transmisyjnego w sieciach heterogenicznych umożliwia poprawę parametrów

sieci w stopniu wyższym niż wynikający wprost z poszerzenia widma transmisyjnego.

iv STRESZCZENIE

Dodatkowe korzyści powstają dzięki zastosowaniu metod koordynacji wykorzystania

pasm. W rozprawie zaproponowano metody tego typu odnoszące się do równoważenia

obciążenia ruchem sieciowym oraz koordynacji interferencji dla sieci REN.

W rezultacie prac opisanych w rozprawie zaproponowano kompletny schemat

RRM dla sieci REN, w szczególności dla sieci REN funkcjonujących w oparciu

o wielopasmową organizację widma. Zaproponowane rozwiązania zostały

przygotowane i przetestowane w odniesieniu do systemu LTE-A, jednakże koncepcje

leżące u ich podstawy są uniwersalne. Umożliwia to ich implementację także w innych

systemach 4G.

v

Acknowledgements

First and foremost, the author would like to thank prof. Krzysztof Wesołowski. His

supervision and guidance through the world of academic research were indispensable

during preparation of this dissertation and the whole process of the doctoral studies.

Many of the concepts described in this dissertation have been originally created

by the author while working as a representative of the Nokia Siemens Networks

company in the European Commission’s 7th

Framework Program project: Advanced

Ratio Interface Technologies for 4G Systems (ARTIST4G). During this time the author

frequently engaged in many interesting discussions with multiple peers. For all those

discussions that were source of inspiration and reflection the author would like to thank

all the Nokia Siemens Networks-Research team members and ARTIST4G partners.

Special thanks go here to Adrian Bohdanowicz, who enabled this work at its early stage

and kept on motivating its finalization until the very end.

The author is a scholar within Sub-measure 8.2.2 Regional Innovation Strategies,

Measure 8.2 Transfer of knowledge, Priority VIII Regional human resources for the

economy Human Capital Operational Programme co-financed by European Social Fund

and state budget.

vi ACKNOWLEDGEMENTS

vii

List of Acronyms

3GPP 3rd

Generation Partnership Project

4G 4th

Generation

A2A Access-to-Access

A2B Access-to-Backhaul

AC Access

ACIR Adjacent Channel Interference Ratio

ACLR Adjacent Channel Leakage Ratio

ACS Adjacent Channel Selectivity

AF Amplify-and-Forward

AI Antenna Isolation

AMC Adaptive Modulation and Coding

AoA Angle of Arrival

AoD Angle of Departure

ARTIST4G Advanced Radio Interface Technologies for 4G Systems

BE Best Effort

BH Backhaul

BS Base Station

BSR Buffer Status Report

CA Carrier Aggregation

CAR Carrier Aggregation for Relaying

CC Component Carrier

CDF Cumulative Density Function

CDMA2000 Code Division Multiple Access 2000

CF Compress-and-Forward

CIS Cumulated Interference Strength

CLB Carrier Load Balancing

CoMP Coordinated Multi-Point

CSG Closed Subscriber Group

CSI Channel State Information

D2A Direct-to-Access

D2B Direct-to-Backhaul

D2D Device-to-Device

DF Decode-and-Forward

DL Downlink

EESM Exponential Effective SINR Mapping

eNB eNodeB

EPC Enhanced Packet Core

ET Elastic Traffic

viii LIST OF ACRONYMS

EVM Error Vector Magnitude

FDD Frequency Division Duplex

FDM Frequency Domain Multiplexing

FF Fast Fading

FFR Fractional Frequency Reuse

FTP File Transfer Protocol

GBR Guaranteed Bit Rate

GSM Global System for Mobile communications

HARQ Hybrid Automatic Repeat Request

HD High Definition

HetNet Heterogeneous Network

HFR Hard Frequency Reuse

HII High Interference Indicator

HSPA High Speed Packet Access

ICI Inter-Cell Interference

ICIC Inter-Cell Interference Coordination

IEEE Institute for Electrical and Electronics Engineers

IMS IP Multimedia Subsystem

IMT-A International Mobile Telecommunications-Advanced

IP Internet Protocol

IPTV Internet Protocol Television

IrDA Infrared Data Association

ISD Inter-Site Distance

ISR Interference-to-Signal Ratio

ITU International Telecommunication Union

ITU-R International Telecommunication Union, Radiocommunication section

KPI Key Performance Indicator

LOS Line of Sight

LTE Long Term Evolution

LTE-A Long Term Evolution – Advanced

MAC Medium Access Control

MBSFN Multimedia Broadcast over Single Frequency Network

MC Multi-Carrier

MH Mobile Hashing

MI Minimum Interference

MIMO Multiple-Input-Multiple-Output

ML Minimum Load

MMF Max-Min Fair(ness)

MS Mobile Station

nET non-Elastic Traffic

NF Noise Figure

LIST OF ACRONYMS ix

NLOS Non-Line of Sight

OAM Operation and Maintenance

OI Overload Indicator

OOB Out-of-Band

PCC Primary Component Carrier

PDB Packet Delay Budget

PDF Probability Density Function

PER Packet Error Rate

PF Proportional Fair(ness)

PL Pathloss

POF Price of Fairness

PRB Physical Resource Block

PS Packet Scheduling

PSD Power Spectral Density

QCI QoS Class Identifier

QoE Quality-of-Experience

QoS Quality-of-Service

RAN Radio Access Network

REN Relay-Enhanced Network

RLN Relay-Less Network

RN Relay Node

RNTP Relative Narrowband Transmission Power

RR Round Robin

RRC Radio Resource Control

RRM Radio Resource Management

Rx Reception

SC Single-Carrier

SCC Secondary Component Carrier

SD Standard Definition

SF Slow (or Shadow) Fading

SFR Soft Frequency Reuse

SI Self Interference

SINR Signal-to-Interference-and-Noise Ratio

SLA Side Lobe Attenuation

SN Source Node

SNR Signal-to-Noise Ratio

SON Self-Organizing Network

TDD Time Division Duplex

TDM Time Domain Multiplexing

TN Target Node

TTI Transmission Time Interval

x LIST OF ACRONYMS

TTL Time to Live

Tx Transmission

UE User Equipment

UL Uplink

UMTS Universal Mobile Communications System

VoIP Voice over Internet Protocol

WiMAX Worldwide Interoperability for Microwave Access

WLAN Wireless Local Area Network

WPAN Wireless Private Area Network

WWAN Wireless Wide Area Network

xi

Mathematical Notation

vector of consecutive natural numbers from to

proportional to

asymptotic to

multiplication of variables and

average value of variable

change of value of variable

round of towards higher integer

round of towards lower integer

cardinality of set

set of variables for which relation is true

statistical function calculated over set of samples , with

expected value of variable

Jain fairness index of variable

probability of event

variance of variable

cumulative distribution function of variable

probability density function of variable

price of fairness function of resource allocation scheme (for definition

see equation (3.15))

binomial coefficient

natural logarithm of variable

set of real numbers

Lagrange function of arguments , , , …

channel capacity function mapping channel quality such as SNR or

SINR on the channel capacity, example of the function is the

Shannon function [94]:

Exponential effective SINR mapping function (for definition see equation

(A.1))

modulo

xii MATHEMATICAL NOTATION

xiii

Table of Contents

Abstract .............................................................................................................................. i

Streszczenie ..................................................................................................................... iii

Acknowledgements ........................................................................................................... v

List of Acronyms ........................................................................................................... vii

Mathematical Notation ................................................................................................... xi

Table of Contents ......................................................................................................... xiii

Chapter 1 Introduction ................................................................................................ 1

1.1 Preface ................................................................................................................ 1

1.2 Purpose and Theses ............................................................................................ 2

1.3 Outline of the Contents ...................................................................................... 3

Chapter 2 Baseline System and State of the Art Solutions ....................................... 7

2.1 Introduction ........................................................................................................ 7

2.2 Wireless Relaying .............................................................................................. 8

2.2.1 Classification of Relaying Concepts ............................................................ 9

2.2.2 LTE-A Relaying Implementation .............................................................. 16

2.3 Evolution of RRM Concepts ............................................................................ 18

2.4 Summary .......................................................................................................... 22

Chapter 3 Resource Management in 4G Cellular Networks .................................. 25

3.1 Introduction ...................................................................................................... 25

3.2 Classical View on Resource Management ....................................................... 26

3.3 QoS-Aware Resource Management ................................................................. 31

3.3.1 Introduction to the Utility Theory .............................................................. 32

3.3.2 Proposals of Utility Functions .................................................................... 35

3.4 Resource Management for Relaying ................................................................ 43

3.4.1 General Framework of Relaying RRM ...................................................... 43

3.4.2 Extension of Utility Theory to Relaying .................................................... 51

3.5 Summary .......................................................................................................... 57

Chapter 4 Single- and Multi-Carrier Relaying Schemes ........................................ 59

4.1 Introduction ...................................................................................................... 59

xiv TABLE OF CONTENTS

4.2 Single-Carrier Systems with Relaying ............................................................. 60

4.2.1 In-Band Resource Partitioning ................................................................... 60

4.2.2 RRM under In-Band Relaying Constraints ................................................ 63

4.3 Multi-Carrier Systems with Relaying .............................................................. 75

4.3.1 Multi-Carrier Resource Partitioning ........................................................... 75

4.3.2 Inter-Carrier Self-Interference .................................................................... 80

4.4 Transmission Delays over Relayed Links ........................................................ 84

4.5 Summary .......................................................................................................... 93

Chapter 5 Carrier-Based RRM Coordination ......................................................... 95

5.1 Introduction ...................................................................................................... 95

5.2 Carrier-Based Load Balancing ......................................................................... 95

5.2.1 Principles .................................................................................................... 95

5.2.2 Proactive Load Balancing .......................................................................... 97

5.2.3 Load-Aware Adaptation ........................................................................... 111

5.3 Inter-Cell Interference Coordination .............................................................. 119

5.3.1 Principles .................................................................................................. 119

5.3.2 Carrier-Based ICIC Concept Proposal ..................................................... 121

5.3.3 Evaluation of the Carrier-Based ICIC Concept ........................................ 124

5.4 Summary ........................................................................................................ 129

Chapter 6 Summarizing the Results and Conclusions .......................................... 131

References ..................................................................................................................... 135

List of Figures ............................................................................................................... 145

List of Tables ................................................................................................................. 149

Appendix A System Level Simulator Description ................................................. 151

A.1 Simulation Methodology ................................................................................ 151

A.2 Network Model .............................................................................................. 154

A.3 Traffic Models ................................................................................................ 157

A.4 Propagation Models ........................................................................................ 158

A.5 Results Reliability Discussion ........................................................................ 160

1

Chapter 1 Introduction

1.1 Preface

The last two decades showed how important it is for people in developed societies to

have a mobile access to communication services. Nowadays, the need is satisfied by

various radiocommunication technologies on several levels [85]:

Wireless personal area networks (WPAN) providing short range wireless

connectivity (e.g. IrDA, Bluetooth, ZigBee systems),

Wireless local area networks (WLAN) providing medium range wireless

connectivity (e.g. WiFi system),

Wireless wide area networks (WWAN) supporting mobile connectivity (e.g.

GSM, UMTS, and CDMA2000 systems).

Functionality of those wireless communication systems is enabled by the effort

spent on research in the area of radiocommunication. Especially important are

developments done in the field of RRM. The RRM functionalities shape performance

offered to users in a network and determine the overall efficiency of wireless systems.

At the moment of writing this dissertation the standardization bodies such as the

3rd

Generation Partnership Project (3GPP) and the Institute of Electrical and Electronics

Engineers (IEEE) work on further development of the 4G radiocommunication systems.

The 4G systems commonly consider a new paradigm in network deployments –

heterogeneous networks (HetNets). A HetNet is a network built with traditional high

range access points (macro base stations) and new low power access points (e.g. femto-

or pico-cells) [92]. The co-deployment of access points of various types and cell sizes in

a common area creates the need for a new approach to RRM. A required feature of the

new RRM functionalities is to enable coordination of the multitude of co-deployed cells

for a network-wide improved performance.

A special case of HetNets are relay-enhanced networks (RENs). An REN is a

network involving relay nodes (RNs), i.e. access points with a backhaul link established

over the air interface (radio or optical link) to a different access point. The operation of

RNs adds new dimensions to the RRM known from the traditional, i.e. relay-less

networks (RLNs). In RENs the RRM functionalities at standalone base stations (BSs)

and at RNs need to be coordinated to secure satisfactory performance for the BS-

connected as well as the RN-connected mobile stations (MSs).

This dissertation addresses the problem of RRM optimization for RENs in the

presence of dynamic radio and traffic conditions. Such definition of the problem is in

line with the recent trends in evolution of RRM concepts, i.e. the self-optimizing

2 CHAPTER 1INTRODUCTION

network and cognitive radio frameworks. Specifically, the RRM solutions developed as

part of this work are adaptive with respect to the natural network traffic fluctuation

cycles, as well as unpredicted changes in the network deployment, e.g. BS malfunctions

or uncoordinated activation of new access points.

Furthermore, this work assumes a holistic view on the evolution of cellular

systems by consolidating various features proposed for the 4G systems. Especially,

operation of RNs in systems based on multi-carrier spectrum arrangement is considered

here. The multi-carrier spectrum organization enables various RRM techniques to be

involved that are not available in currently dominant single-carrier networks. The multi-

carrier RRM techniques for RENs developed as part of this work are:

carrier aggregation for relaying (CAR),

carrier-based load balancing (CLB) and

carrier-based inter-cell interference coordination (ICIC).

Evaluations of the proposed concepts are done with respect to the 3GPP Long Term

Evolution-Advanced (LTE-A) system standard of Release 10 and beyond. The concepts

themselves, however, should be applicable also to other 4G systems.

1.2 Purpose and Theses

This dissertation presents summary of studies performed by its author in the area of

RRM for cellular networks incorporating advanced RNs. The main problems

investigated as part of this study are:

operation of RNs based on multi-carrier spectrum arrangement and in

combination with the carrier aggregation technique, and

adaptive RRM algorithms for RENs, with special focus on the problems of QoS

satisfaction and performance fairness provisioning with respect to dynamic radio

and traffic conditions.

Purpose of this study is to develop solutions improving performance of the future

RENs. Specifically, this study is conducted with respect to the technology roadmaps

specified by the International Telecommunication Union (ITU), the 3GPP and the IEEE

standardization bodies for evolution of the 4G radiocommunication systems. This

ensures high compatibility of the proposed concepts with future cellular systems.

CHAPTER 1 INTRODUCTION 3

With respect to the purpose of this work and the aforementioned study problems

the theses of this dissertation are:

1. Relay-enhanced networks with multi-carrier spectrum arrangement can

achieve higher overall performance than single-carrier networks with the

same total frequency bandwidth allocation. The additional gains in form of

increased system capacity, QoS satisfaction and performance fairness are

available if channel- and load-aware carrier-based resource management

techniques are employed, particularly such as those proposed in this

dissertation.

2. Static or semi-static network configuration that is dominant in the today

radiocommunication networks is sub-optimal and incapable of dealing

with the dynamics and the heterogeneity of the future cellular systems.

Dynamic management concepts based on instantaneous system status

knowledge and decentralized decision making can improve QoS

provisioning and its fairness to all users of the system regardless of their

traffic type, location in the network, and type of the serving access point.

1.3 Outline of the Contents

The main content of this dissertation is divided into four parts that correspond to the

chapters of this document:

Baseline system and state of the art solutions,

Resource management in 4G cellular networks,

Single- and multi-carrier relaying schemes,

Carrier-based RRM coordination.

Chapter 2 presents a precise overview of the most relevant state of the art

concepts in the area of RRM and relaying for cellular systems. This is the starting point

for the work described in this dissertation. The main baseline is the 3GPP LTE-A

radiocommunication system. Chapter 2, however, focuses not only on the LTE-A system

and its features but is also a survey of related concepts considered in other

radiocommunication systems (e.g. the Worldwide Interoperability for Microwave

Access, WiMAX) or existing just on the conceptual level. Particularly, Section 2.2

presents a classification of the available relaying concepts together with a theoretical

performance analysis of the most common schemes. Details of the relaying technique

implementation in the LTE-A system are also included in this section. Further,

Section 2.3 is a survey of different approaches to RRM with a special focus on adaptive

RRM concepts and the recent trends in this field.


Chapter 3 formulates the RRM framework for the next generation cellular

networks. In the discussion diverse needs of RRM actors are discussed. This leads to

formulation of the basic rules for an efficient RRM. The RRM efficiency is analysed

with respect to the overall system performance and the single user perceived

performance fairness (see Section 3.2). The analysis is carried on next with respect to

heterogeneous traffic schemes, i.e. scenarios involving services with diverse QoS

requirements (see Section 3.3). To resolve scenarios involving mixtures of service types,

utility theory is employed. On the basis of the utility theory a universal procedure for an

iterative QoS-aware resource allocation is proposed. The RRM framework formulation

is extended next (in Section 3.4) to take into account specific aspects of operation of

RENs. In this context interdependencies between various types of nodes and links are

analysed. Finally, a multi-level RRM procedure for RENs is proposed based on

distribution of utilities over a multi-hop relaying topology.

After having formulated principles of the RRM for RENs in Chapter 3, Chapter 4

concentrates on practical solutions for its implementation in cellular systems. Here the

RRM restrictions existing in real systems are considered. Firstly, in Section 4.2 the

RRM options for a single-carrier REN operation are presented. In the analysis,

shortcomings of the single-carrier RN configurations are highlighted with respect to the

QoS satisfaction and the performance fairness provisioning. Secondly, in Section 4.3

RRM enhancements related to the availability of multiple carriers are investigated. In

this section various options for carrier-based resource allocation for RNs are proposed

and compared. This includes application of the carrier aggregation technique for RNs

which, to the best of the author's knowledge, is explicitly studied for the first time as part

of this work. Author’s own contributions to this topic are also: analysis of inter-carrier

interference impact on the multi-carrier RN operation, performance analysis of multi-

hop relaying topologies with respect to various RN configurations and detailed timing

analysis of the single- and multi-carrier RN configurations.

Chapter 5 presents proposals for carrier-based RRM coordination schemes. In

this chapter the ability of RENs to adapt to changing conditions is investigated. Firstly,

in Section 5.2 carrier-based load balancing methods are proposed. The purpose of those

methods is to provide means of steering resource allocation with respect to traffic

requirements, i.e. to provide radio resources exactly where and when they are the most

needed. The proposed load balancing methods include proactive and reactive RRM

schemes that can be used jointly or separately in a system. Next, in Section 5.3 a concept

of carrier-based interference coordination for RENs is proposed. In contrast to relay-less

networks, the multi-hop nature of relay-enhanced links forces to perform the

interference coordination not only with respect to the end-user perceived signal quality,

but also with respect to the RN backhaul link capacity. The proposed coordination

algorithm takes into account this specific requirement of RENs. The carrier-based inter-

cell interference coordination (ICIC) concept is also investigated from the point of view

CHAPTER 1 INTRODUCTION 5

of the system status information provisioning in RENs. In this field three RRM

approaches are compared: centralized, distributed and autonomous.

Finally, the dissertation is summarized in Chapter 6. In the concluding chapter

the two theses of this dissertation are faced against the outcomes of the conducted study.

In addition, future research options in the field of RRM for RENs are sketched.


7

Chapter 2 Baseline System and State of the Art Solutions

2.1 Introduction

The baselines for the work described in this dissertation are the 4G cellular systems

developed with accordance to the International Mobile Telecommunications-Advanced

(IMT-A) framework. The IMT-A framework has been specified by the ITU

Radiocommunication section (ITU-R) to shape evolution of the next generation

radiocommunication systems. The IMT-A framework [63] defines the main

requirements for the 4G systems. The most important requirements are:

packet switched all-Internet Protocol (IP) network, enabling interoperability with

existing radio systems and supporting a wide range of services,

downlink peak data rates of 100 Mbit/s for mobile and 1 Gbit/s for stationary

users,

support for high speed mobility up to 350 km/h,

peak spectral efficiencies of 15 bit/s/Hz in downlink and 6.75 bit/s/Hz in uplink

with improved cell-edge and average spectral efficiencies,

scalable system bandwidth up to 40 MHz with recommended support up to

100 MHz.

Currently, the two main systems conforming to IMT-A requirements are the

LTE-A [1] and the WiMAX Release 2 (IEEE 802.16m) [105]. To fulfil the IMT-A

requirements both systems include a similar set of enhancement techniques [77, 86,

106], i.e.:

Advanced multi antenna techniques (multiple-input-multiple-output, MIMO), i.e.

more efficient utilization of the spatial domain in form of beamforming and

spatial multiplexing schemes (multi-user MIMO, multi-stream transmissions).

Carrier aggregation (CA), i.e. utilization of radio resources from two or more

component carriers for carrying transmissions of a user. The carrier aggregation

technique enables flexible spectrum allocation and the ITU-R requested

bandwidth extension in a backward compatible manner.

Heterogeneous networks (HetNets), i.e., support for deployments including low

power nodes such as femto and relay nodes. The low power nodes enable a cost-

efficient network coverage extension as well as the cell-edge and overall network

capacity enhancement.

8 CHAPTER 2 BASELINE SYSTEM AND STATE OF THE ART SOLUTIONS

Improved interference mitigation techniques, including interference cancelation

and inter-cell RRM coordination (e.g. the coordinated multi-point, CoMP,

concept or advanced time and/or frequency domain inter-cell interference

coordination, ICIC, concepts).

This dissertation investigates combination of some of the above listed techniques

in a common system. Explicitly, this work considers operation of advanced RNs in

systems utilizing multiple frequency carriers (including the carrier aggregation

technique). With respect to such systems, carrier-based RRM coordination schemes are

investigated with the aim to support wide landscape of services in a QoS-aware manner.

The main baseline for the work described in this dissertation is Release 10 of the

LTE system, the first one supporting the LTE-A features. All the concepts and

evaluations presented in this document are done with respect to this baseline. The

developed concepts, however, are prepared with consideration of the beyond-LTE state

of the art solutions. The state of the art solutions, which are the most relevant for this

dissertation, are subsequently described in this chapter.

2.2 Wireless Relaying

The theoretical basis of relaying was developed in the 1970’s and the early 1980’s. For

the first time the concept of a three-node transmission was described by

Van Der Meulen in [76]. Later, performance of simple relay-enhanced channels was

evaluated inter alia by Cover and El Gamal [39]. After those first studies the relaying

concept was abandoned up to the late 1990’s. The reason for the cessation of the studies

on relaying for so long was probably the lack of firm applications for the concept visible

at that time [108].

Relaying was brought back with the development of mobile radio systems, for

which it is envisioned as a cost efficient solution for coverage enhancement. From the

beginning the wireless relaying concept is developed in two directions: ad hoc and fixed

infrastructure networks [108]. The ad hoc solution focuses on dynamic device-to-device

(D2D) routing. Details of the concept can be found, e.g., in the works of Doppler

et al. [43-45]. On the other hand, the fixed infrastructure solution focuses on utilizing

dedicated devices – the relaying stations, or simply the relay nodes. At the moment the

later solution has taken the lead as it fits perfectly into the architecture of existing

cellular networks and has lesser impact on standards of the existing systems. The D2D

concept, however, is not abandoned and might be included in future generation systems,

e.g. the 5th

generation (5G) [75, 83].

In the following section various concepts of the infrastructure-based relaying are

presented. Theoretical models of operation are given for the most common relaying

types, together with an analysis of the main benefits available from their application.

Later, details of the relaying implementation in the LTE-A system are given.

CHAPTER 2 BASELINE SYSTEM AND STATE OF THE ART SOLUTIONS 9

2.2.1 Classification of Relaying Concepts

The principle of relaying is introduction of a supporting node (the relay node) in the

typical two-node (source-target) communication channel. Depending on the specific

implementation of the relaying functionality the nature of the support might be various,

as well as the resulting benefits for the system. With respect to the impact on a cellular

system the basic relaying scenarios are (see Figure 2-1):

Coverage extension, with RNs extending communication range of a base station

(BS) by providing additional coverage,

Capacity enhancement, with RNs improving source-target connection quality

within the normal BS coverage region.

Figure 2-1 Application scenarios for relaying in cellular systems

In the coverage extension scenario the RN is the access point providing coverage

for mobile stations (MSs). This implies that not only the data transmissions but also the

control information required for correct reception of those transmissions needs to be

provided from the RN. In contrast, in the capacity enhancement scenario MSs remain

within the BS coverage region, thus, it is only required from the RN to enhance quality

of the data transmissions.

The two basic relaying scenarios induce the first level of classification of the

relaying techniques [2]:

Type-1 relays (a.k.a. non-transparent relays [2, 84]), i.e. the RNs that operate on

data and control planes and can be applied in both the coverage extension and the

capacity enhancement scenarios,

Type-2 relays (a.k.a. transparent relays [2, 84]), i.e. the RNs that operate on data

plane only and require the BS to communicate with MSs directly to provide

control information. The Type-2 relays are incapable to operate in the coverage

extension scenario.


The key point in this classification is that only the Type-1 RNs are capable

(though not implicitly) to execute individual RRM procedures. The Type-2 RNs, on the

other hand, are dependent on the BS RRM procedures. For this reason, the investigations

described further in this dissertation consider the Type-1 RNs only.

Every RN operates on two link types. On one side it receives transmissions from

the source node(s) (SN) on the feeder link. On the other side it transmits to the target

node(s) (TN) on the sink link. A side effect of the RN operation is interference coupling

from the RN transmitter to receiver (a.k.a. the RN self-interference). A model of the

relay-enhanced channel including the three link types is depicted in Figure 2-2.

Figure 2-2 Model of the relay-enhanced communication channel

The mathematical description for the model is specified with the following formulas:

(2.1)

where and are, respectively, the signals transmitted from the SN and the RN,

and are the signals received, respectively, by the RN and the TN, , , and

are the impulse responses of the direct, feeder, sink and the RN self-interference links

respectively, and are the noise signals received, respectively, by the RN and the

TN, and is the relaying function.

Considering the relay-enhanced channel model defined as in formula (2.1) the

relaying techniques can be further classified based on the form of the relaying function

. The relaying function defines what processing is applied by the RN to the

forwarded signals. In the state of the art literature multitude of options can be found for

the relaying function. All of those, however, can be classified as three general relaying

functionalities [97]:

Amplify-and-forward (AF) relaying,

SN TN

RNFeeder link Sink link

Direct link

Self-interference link


Decode-and-forward (DF) relaying,

Compress-and-forward (CF) relaying.

Amplify-and-Forward Relaying

The AF relaying employs the simplest signal processing – analogue amplification. The

signals transmitted from the AF RN on the sink link are an amplified version of the

signals received by the AF RN on the feeder link. Therefore, the AF relaying function is:

(2.2)

where is the AF relaying gain.

The AF relaying is simple, but also not too effective. As no advanced signal

processing is applied, the AF RN is unable to separate the desired signals from noise and

interference. Therefore, an AF RN does not improve signal quality in the presence of

high noise on the RN feeder link.

An additional limiting factor for the AF RNs is the RN self-interference. In case

of strong coupling of the RN feeder and sink links (i.e. high ) excitation of the AF RN

amplifier may occur. Stable operation of the AF RN is achieved only if its gain ( ) is

limited by the impulse response of the RN self-interference link, as defined by the

following formulas:

subject to

(2.3)

where is the effective AF RN gain.

Following the analysis presented by Riihonen and Wichman in [89] the end-to-

end quality of an AF relay-enhanced channel can be formulated as:

(2.4)

where , , and are signal-to-noise ratios (SNRs) of the AF relay-enhanced

channel, the direct source-target link, the relay feeder link and the relay sink link,

respectively.


Based on the above formula the following characteristic of the AF relay-

enhanced channel can be made:

AF RNs support coverage extension:

(2.5)

AF RNs provide gains in the end-to-end channel SNR only if SNR of the RN

feeder link is better than the SNR of the direct source-target link:

(2.6)

In case of a weak RN sink link, benefits of using AF relaying are negligible even

in case of a good relay feeder link:

(2.7)

The above characteristics of the AF relay-enhanced channel are further depicted in

Figure 2-3.

Figure 2-3 Performance of the AF relay channel as a function of the RN feeder and sink link SNRs

Decode-and-Forward Relaying

A second view on relaying presents the DF functionality. In the DF approach signals

received by the RN on the feeder link are fully decoded before they are forwarded

towards the target node. This allows regeneration of signals, i.e. separation from

interference and noise. Error correction can be also performed. Furthermore, the DF

relaying enables adaptation of encoding independently for the RN feeder and sink links.


Especially in case of significant differences in quality of the two links the individual

adaptation of encoding provides improvements in terms of transmission robustness and

efficiency. On the other hand, the main drawbacks of the DF approach are:

increased complexity of the RN device,

processing delay (retransmission is not performed in the same time slot as the

original transmission),

need for buffering of the relayed data.

Due to the processing applied on the forwarded signal by the DF RN and the

introduced delay, signals received by the target node from the RN and the source node

are different and cannot be implicitly combined. As a result, signals on the direct and

sink links need to be considered as mutually interfering. This decreases the effective

quality of those links according to the following signal to interference and noise ratio

(SINR) formulas:

(2.8)

where is the SINR of the direct source-target link in the presence of a DF RN, and

is the SINR of the DF RN sink link considering interference from the direct source-

target link.

Furthermore, the Type-1 DF RN functionality implies that it has to be decided

a priori whether the target node is communicating with the source node directly or via

the RN. Typically, in cellular systems this is decided based on the highest signal power

criterion [3]. In the presence of a DF RN this criterion can be formulated as:

(2.9)

where is the SINR observed by the target node in the presence of a DF RN.

In contrast to the AF RNs, the DF RNs can avoid self-interference by employing

resource partitioning [58]. The resource partitioning means allocation of orthogonal sets

of radio resources to the RN feeder and sink links. The resource partitioning can be done

in the time and/or frequency domain. DF RNs employing the time domain resource

partitioning are called the in-band RNs and the DF RNs employing the frequency

domain resource partitioning are called the out-band RNs [2]. Comparative study of the

two resource partitioning schemes done by the author of this dissertation is presented

in [58] and [25]. Detailed comparison of the two schemes is also included in Chapter 4

of this dissertation.


Similarly as for the AF relays, quality of both the RN feeder and sink links

determines the end-to-end performance of the DF relay-enhanced channel. The

transmissions on the two RN links are independent in time and in terms of encoding.

The data flows on the two links are, however, interconnected via the RN buffer. The

flow continuity principle requires that the data rates on the RN feeder and sink links

need to be matched (at least with respect to the RN buffer filling/emptying times). If this

requirement is not fulfilled, over- or underflows of the RN buffer may occur, thus,

limiting the effective data rate. Study of the DF RN buffering impact on the overall

performance of relayed transmissions is presented, e.g., by Vitiello et al. in [98].

Assuming an optimal resource partitioning, i.e. one that provides the optimal RN

buffer operation, the ergodic end-to-end capacity of a DF relay-enhanced channel is

described by the following formula [32]:

(2.10)

where is the ergodic capacity of the DF relay-enhanced channel, is the ergodic

capacity of the DF RN feeder link, and is the ergodic capacity of the DF RN sink

link.

In case the resource partitioning for the RN feeder and sink links is not applied,

the end-to-end capacity of the DF relay-enhanced channel is minimum over the

capacities of the two component links:

(2.11)

where is capacity of the DF RN feeder link in the presence of RN self-interference.

Without the resource partitioning it has to be considered, however, that the SINR

of the DF RN feeder link is reduced by the RN self-interference as accounted for in

the following formula:

AP AP

(2.12)

where is SINR of the DF RN feeder link without the RN self-interference, and is

SNR of the RN self-interference link.

The impact of the RN self-interference on the end-to-end throughput of the DF

relay-enhanced channel is depicted in Figure 2-4. The example assumes equal ergodic

capacities of the RN feeder and sink links of 4 bit/s/Hz (typical relay-enhanced network

conditions based on the analysis presented by the author in [59]). As presented in

Figure 2-4, impact of the RN self-interference on the end-to-end performance of the DF


relay-enhanced channel increases with the resource allocation to the RN feeder link.

This is a result of increasing probability for the feeder and sink links to be operated on

overlapping resources and, thus, be mutually interfering.

Figure 2-4 Impact of self-interference on DF relay-enhanced channel capacity

Figure 2-4 illustrates that the resource partitioning approach provides gains for

the DF RNs mainly in case of strong RN self-interference coupling. If the RN self-

interference is week (e.g. if there is sufficient separation provided between the RN

feeder and sink link antennas), the resource partitioning approach is not recommended.

However, in typical RN deployment scenarios (e.g. on street lampposts) the high

separation between the RN antennas cannot be assumed. In such cases the resource

partitioning approach should be used.

The resource partitioning for the DF RNs is one of the main RRM problems for

RENs. It is discussed by the author in [59] and also together with other authors in [26].

Further analysis of this problem is included in Section 3.4 of this dissertation.

Compress-and-Forward Relaying

The CF relaying can be considered as an intermediate approach between the AF and the

DF relaying schemes. A CF RN receives signals from the SN and processes it. The

processing involves a certain coding scheme that enables cooperation between the RN

and the SN in the transmission delivery to the target node. The most common operation

schemes for the CF RNs are [27, 97]:

Coding-based cooperative relaying, i.e. the RN cooperates with the source node

to apply a distributed coding scheme to the forwarded data. The coding enables

the target node(s) to utilize jointly signals from the source node and one or more


RN(s) for reception and decoding of the transmitted data. The coding-based

cooperation may utilize, e.g., such coding schemes as: space-time coding [79,

80], distributed turbo coding [62, 81] or network coding [61, 104].

Selective relaying, i.e. the RN forwards transmission to the target node only if

the target node fails to correctly receive transmissions directly from the source

node. In such case the RN may provide retransmission of either full set of data or

its fraction (dynamic hybrid automatic repeat request, HARQ) [103]. The

selective relaying may also be used in the form of dynamic path selection, i.e.

dynamic selecting of the node delivering transmission to the target node based on

the instantaneous channel quality information [97].

As multiple variations of the CF relaying schemes exist, there is no one common

formula for capacity of such a relay-enhanced channel. In this work the focus is put on

the non-cooperative relaying schemes rather than the cooperative ones, therefore the CF

relaying is not considered further in this dissertation. The reader interested more in the

topic of CF relaying is advised to look into [97] for classification and detailed analysis

of various CF relaying schemes, and into [27] for an overview of some of the latest

concepts for the Type-2 cooperative relaying.

This section has presented a classification of the state of the art relaying concepts

with the basic performance analysis of the most common configurations. As can be

noticed, the number of options with relaying is high. In the remaining part of this

dissertation the focus is put on one specific configuration, i.e. the Type-1 DF relaying. In

the Type-1 DF configuration the RNs are autonomous access points, yet dependent on

the traditional BS operation. This hierarchical organization of the network is a very

interesting case for studies on system-wide RRM optimization. Furthermore, the Type-1

DF RNs are common in the existing IMT-A systems, what enables direct application of

the concepts developed as part of this work in real networks.

2.2.2 LTE-A Relaying Implementation

The LTE-A system standard in the current form supports two relaying configurations:

(1) the AF relaying and (2) the Type-1 DF relaying (in-band, out-band and without

resource partitioning [2]). The AF RNs are called in the LTE-A nomenclature the

analogue repeaters. Protocol-wise the analogue repeaters are transparent for any

transmissions. Therefore, they do not require explicit standardization and, thus, are

implicitly supported since the first release of the LTE system standard (Release 8) [4]. In

contrast, the LTE-A DF RNs are advanced digital devices including the full

communication protocol stack of a BS. For this reason they are often called the self-

backhauling BSs. Introduction of the Type-1 DF RNs was done in Release 10 of the

LTE-A standard. This release of the LTE-A standard introduces modifications of both


the radio access network (RAN) [3, 5] and the Enhanced Packed Core (EPC) [6] to

support operation of advanced RNs.

The LTE-A DF RN communicates on one side with a BS (in the 3GPP

framework called the eNodeB, eNB) on the backhaul (BH) link, and on the other side

with MSs (in the 3GPP framework called the user equipments, UEs) on the access (AC)

link (see Figure 2-5). The RN BH link supports the RN feeder link for downlink

transmissions (eNB is the source node) and the RN sink link for uplink transmissions

(eNB is the target node). Correspondingly, the RN AC link supports the RN sink link for

downlink transmissions (UE is the target node) and the RN feeder link for uplink

transmissions (UE is the source node).

Figure 2-5 LTE-A relay-enhanced network model

The full BS protocol stack enables the LTE-A DF RNs to perform autonomous

RRM on its AC link. This includes packet scheduling (PS) and adaptive modulation and

coding (AMC) performed at the medium access control (MAC) protocol layer [7], as

well as connection management and overall resource management (power control and

resource allocation) performed at the radio resource control (RRC) protocol layer [8].

From the point of view of the eNB the DF RN has the functionality of a UE. This

includes extension of the eNB RRM procedures targeting UEs to also support the RNs.

In this context, the eNB is aware which UEs are in fact RNs and the eNB RRM

procedures can be adapted to provide dedicated support for the RN operation. The

LTE-A system standard, however, does not provide any dedicated RRM mechanisms for

this purpose. In the up-to-date 3GPP standardization work related to relaying the

coverage extension scenario is prioritized without explicit support for relaying

connection quality. Solutions providing capacity and QoS enhancements with respect to

relaying are left for further studies [9]. It is, inter alia, the purpose of this work to

develop such solutions.

In the LTE-A Release 10 standard configuration of the RN operation is

practically limited to optimization of the RN BH/AC link resource partitioning. And yet

it is assumed that this configuration is static or semi-static. Considering the dynamic

UEeNB RN-UE RN-eNB

RN

backhaul

linkaccess link


nature of cellular networks such approach may be sub-optimal. It is also purpose of this

work to provide solutions for dynamic resource partitioning for LTE-A RNs (see

Section 5.2). In this study operation of RNs on multiple frequency carriers is especially

investigated. This is novel with respect to the baseline LTE-A relaying, as in the 3GPP

standardization only the single-carrier RN operation is so far prioritized [2].

Last but not least, the LTE-A system standard supports currently only the so

called two-hop relaying topology, i.e. relaying with just one RN per an end-to-end eNB-

UE connection (see Figure 2-6). The multi-hop topology, i.e. relaying over multiple

consecutive RNs, is an interesting enhancement enabling further benefits for cellular

systems (e.g. as studied by the author in [52]). The limiting factor for the multi-hop

relaying is, however, the lack of effective RRM procedures dedicated for such

topologies. The RRM procedures developed as part of this work are evaluated with

respect to the two-hop and multi-hop tree topology deployments. Their purpose is to

secure high level of QoS satisfaction and its fairness for all users, also those served over

multi-hop links.

Figure 2-6 Two-hop and multi-hop relaying topologies

2.3 Evolution of RRM Concepts

RRM in cellular systems is done on link, cell and system levels. The link level

management has the aim of optimizing parameters of a single transmission with respect

to the expected channel conditions on the radio link. For this purpose, the link level

RRM involves: (1) encoding at the transmitter (e.g. coding, modulation, MIMO pre-

processing) and (2) link level power control. Next, the role of the cell level RRM is to

divide radio resources of an access point between the devices connected to it. The

division should be done in a way that maximizes efficiency of utilization of the

resources and satisfies the users of the cell. Operation of cell level RRM is based on

various scheduling algorithms, mainly in the time and/or frequency domain. Finally, the

system level RRM takes care of optimization of the network-wide performance by

means of an inter-cell coordination. This includes: (1) resource reuse coordination and

(2) cell-wise power control. This section presents an overview of the classical and novel

approaches to the system level RRM. The cell level RRM in form of various PS policies


is discussed in detail in Chapter 3. The link level RRM is not explicitly considered in

this dissertation, as it is beyond the scope of the Type-1 relaying investigations.

Baseline Inter-Cell Interference Coordination

The most common system level RRM problem is mitigation of the inter-cell interference

(ICI). A direct approach to the inter-cell interference mitigation is limitation of

transmission power. This approach is, however, not effective in dense deployments (e.g.

in city centres) that are typically interference limited. In the interference limited scenario

proportional reduction of transmission power at all cells has a similar impact on the

interference as on the useful signal. For this reason in the LTE system the power control

approach is practically used only for uplink transmissions, where it provides gains in

form of decreased power consumption and prolonged battery life of mobile devices.

A more efficient approach to inter-cell interference mitigation is coordination of

resource reuse across cells (inter-cell interference coordination, ICIC). This approach is

based on planned division of the system resources into orthogonal subsets and allocation

of different subsets to neighbouring cells. The most basic resource reuse scheme is the

hard frequency reuse (HFR) of a certain reuse factor (e.g. in the GSM system HFR up

to reuse-12 is used [70]). Example of the HFR reuse-3 network planning is depicted in

Figure 2-7.

Figure 2-7 Hard frequency reuse scheme (reuse factor 3)

Drawback of the HFR ICIC scheme is relatively low resource utilization. By

assigning fully orthogonal subsets of resources to neighbouring cells the inter-cell

interference is to a big extent mitigated, thus increasing SINRs in the network. However,

with the reuse of factor only of the system resources is used in each cell.

Considering this the HFR scheme is only beneficial if the gains coming from the


increase of SINRs overcome the capacity loss due to decreased resource utilization. It is

so when the following condition is fulfilled:

(2.13)

where is link SINR with full resource reuse and is SINR of the link with resource

reuse- .

A trade-off between full resource reuse and the HFR scheme are the soft

frequency reuse (SFR) and the fractional frequency reuse (FFR) schemes [48, 71]. In

those ICIC schemes each cell in the network is divided into two regions: cell-centre and

cell-edge. The cell-centre region is considered to be affected by the inter-cell

interference on a minimal level, thus, it can reuse resources used in the neighbouring

cells. The cell-edge regions, on the other hand, experience high inter-cell interference,

thus, inter-cell resource reuse coordination is required in those regions. To satisfy both

cases, the SFR scheme considers reuse- resource allocation for cell-edge regions while

for the cell-centre regions reuse-1 can be used, but with limited transmission power (see

Figure 2-8).

Figure 2-8 Soft frequency reuse scheme (reuse factor 3)

The FFR scheme includes, firstly, resource division for the cell-centre resource

pool and the cell-edge resource pool. Secondly, the cell-centre resource pool is assigned

to all cells with reuse-1, and the cell-edge resource pool is assigned with reuse- (see

Figure 2-9). For both the SFR and FFR schemes it is a matter of RRM optimization to

determine the optimal proportions between the cell-centre and cell-edge regions [48].


Figure 2-9 Fractional frequency reuse scheme (reuse factor 3)

The above described ICIC schemes are effective in case of regular network

deployments based on one type of access points (i.e. homogeneous network). In case of

irregular deployments the planned resource reuse schemes can be to some extent adapted

based on ad hoc system status information. For this purpose the LTE system defines

three types of messages that can be exchanged between neighbouring eNBs for ICIC

coordination [10]:

Relative Narrowband Transmission Power (RNTP), i.e. indication of eNB’s

downlink power restriction per physical resource block (PRB). Can be used for

indication of the downlink resource reuse configuration.

High Interference Indication (HII), i.e. indication of eNB’s uplink interference

sensitivity per PRB. Can be used for proactive uplink ICIC.

Overload Indication (OI) – eNB’s indication of experienced uplink interference

levels per PRB. Can be used for reactive uplink ICIC.

The above described messages enable a certain level of network coordination in

an adaptive manner. It should be, however, considered that (at least in the LTE standard

definition) those messages are of informative nature, i.e. an eNB can use them to

indicate its own state. Whether its neighbouring eNBs engage in a cooperation based on

those messages is beyond the baseline concept.

Modern Network Adaptation Concepts

The modern networks incorporate various types of access points in one region (e.g.

femto, pico or relay nodes) [92] and may even incorporate different systems (e.g. LTE,

HSPA, WiFi). In such heterogeneous networks the basic frequency planning ICIC is

often insufficient and advanced solutions are required.


To cope with the challenges of the modern networks, and also to provide better

support for traditional networks various new concepts are being developed. The general

purpose of the new concepts is to enable more aware and dynamic network

management. In the context of LTE system standardization the approach is called the

self-optimizing network (SON) [22]. The SON concept is a set of techniques providing

means for an automatic problem (malfunction or inefficiency) detection and resolution

in a cellular system. As identified by the 3GPP forum in the technical report [11] the

expected SON use cases are [11]: coverage and capacity optimization, cell load

balancing, energy savings and interference reduction based on autonomous cell

deactivation, automated configuration of cells, neighbourhood detection and ICIC.

The LTE system standard provides enablers for the SON algorithms (interfaces

and communication protocols). Specific optimization algorithms are, however,

implementation specific. Basics of many SON concepts were developed as part of the

European Commission's SOCRATES project. The research initiative is continued now

in form of the SEMAFOUR project.

By analysing the state of the art literature multiple SON-type algorithms can be

found. The algorithms take advantage of various common optimization techniques. Very

often the SON algorithms are based on elements of the game theory [74, 109]. The game

theory provides theoretical background for multi-node optimization procedures typical

for cellular networks. With respect to the game theory each node in the network is

considered as a player. If the players are able to communicate with each other, they can

negotiate access to certain resources. For this purpose they may even form coalitions and

play the so called cooperative games [91]. If the players are not able to communicate,

they play autonomous games. Depending on the specific configuration of the RRM

game (corresponding to the network model) the players may make decisions with

respect to a full or limited system state information [109] and their actions may be either

altruistic or selfish [38, 50].

The next step in the evolution of system level RRM are the solutions based on

the cognitive radio concept. The cognitive radio networks are not based on a static

spectrum arrangement, but rather on opportunistic resource allocation. Such behaviour is

required in case of ad hoc networks or networks involving mobile access points.

According to the cognitive radio concept [23] a node newly activated in a network

should: (1) autonomously detect spectrum arrangement in its location, and (2) select for

its operation the part of the spectrum optimal not only for its own functioning but also

for the overall network performance.

2.4 Summary

This chapter presents the state of the art relevant for the work described in the further

chapters of this dissertation. Firstly, it includes overview, classification and analysis of


the existing relaying schemes, together with details of the relaying implementation in the

LTE-A system. The most relevant state of the art configuration to this work is the self-

backhauling base station concept (Type-1 DF RN) defined in the LTE-A system

standard. With respect to this configuration a number of shortcomings of the existing

standard have been highlighted with the intention to provide solutions further in this

work.

In the second part of this chapter an overview of the existing approaches to the

system level resource management has been presented. The discussion starts with the

classical network planning based coordination schemes and next goes through the

various novel resource management solutions. The classical static or semi-static resource

allocation concepts are sufficient for traditional homogeneous networks. However,

modern heterogeneous networks require dynamic coordination strategies. The current

networks can facilitate the dynamic RRM schemes by providing infrastructure support in

form of inter-node interfaces and central coordination entities. However, as the future

systems are envisioned to incorporate massive deployments of small cells, machine type

communication or direct D2D communication even more autonomous RRM solutions

will be required. The new concepts, such as self-organizing networks, game theory or

cognitive radio define the new paradigm of adaptive network management. The

solutions developed as part of this work also follow this paradigm.


25

Chapter 3 Resource Management in 4G Cellular Networks

3.1 Introduction

Radio resources are the time, frequency and other elementary entities that enable

communication over radio interface. Those resources are typically limited, yet their

availability is deterministic for performance of a radio communication. This relation

leads to definition of the main RRM problem, which can be formulated as follows:

How to allocate scarce radio resources

in order to maximize a certain figure of merit?

The answer to this question is not straightforward as typically contradicting needs of

multiple communicating nodes should be considered. This imposes a requirement for

defining certain RRM policies that prioritize the needs of those nodes and shape

performance they achieve.

In the previous generations of communication systems the RRM problem was

relatively simple as only few types of communication services were in use. However, the

modern communication systems have to cope with a wide landscape of services with

various QoS requirements (i.e. heterogeneous traffic conditions). Depending, if the

services have any specific QoS requirements specified or not, they can be classified

either as elastic or non-elastic traffic.

A service type that does not have any specific QoS requirements is called the

elastic traffic (ET). The maximum transmission time (a.k.a. the latency) for the elastic

traffic data packets is not restricted. However, the user satisfaction level increases with

shorter transmission times. The total data payload is available at the transmission start

time, and thus in principle there are no limitations to the transmission data rate related to

the data generation process. One example of the elastic traffic service is the file transfer

protocol (FTP) service.

The services with QoS requirements are classified as the non-elastic traffic

(nET). The non-elastic traffic may be restricted with respect to the bit rate and/or the

packet delivery time. If the service requires a specific data rate the requirement is

defined as the guaranteed bit rate (GBR) [12]. The GBR requirement is typically related

to the data generation process at the source node or the data processing at the target node

(e.g. live audio/video streaming). In case of some services there might be also the

requirement on the maximum packet latency, in the LTE standards defined as the packet

delay budget (PDB) [12]. While the GBR corresponds to the average data rate, the PDB

sets requirements on the instantaneous effective data rates (delivery time and packet

26 CHAPTER 3 RESOURCE MANAGEMENT IN 4G CELLULAR NETWORKS

error rate). On the basis of the GBR and PDB requirements multiple QoS classes are

defined. Summary of the 3GPP standardized QoS classes is presented in Table 3-1.

Table 3-1 3GPP standardized QoS classes [12]

QoS class

identifier

(QCI)

Priority Bit rate

requirement PDB

1) Packet error

rate (PER) Service example

1 2

GBR

100 ms 10-2 Conversational voice

(live streaming)

2 4 150 ms 10-3 Conversational video

(live streaming)

3 3 50 ms 10-3

Real-time gaming

4 5 300 ms 10-6 Non-conversational video

(buffered streaming)

5 1

Non-GBR

100 ms 10-6

IMS signalling

6 6 300 ms 10-6 Web traffic for privileged

users

7 7 100 ms 10-3

Interactive gaming

8 8 300 ms 10-6 Web traffic for standard

users

9 9 Elastic traffic 1)

Including 20 ms of average delay reserved for the core network signalling

Considering the given characteristics of the elastic and non-elastic services, the

two types of traffic should be handled differently in a system in order to maximize the

overall satisfaction of users. A feature of an efficient RRM should be then to identify the

type of traffic for each user and to adapt resource allocation procedures to the traffic

mixture and radio conditions present in the network. The awareness and adaptiveness are

the key principles of the RRM concept described in this dissertation.

Further in this chapter, answers to two questions are looked for: (1) how to

define a universal figure of merit for various traffic types, and (2) how to perform

system optimization with respect to it. To give answers to those questions, firstly, the

classical approaches to RRM and system optimization are discussed. Secondly, a more

universal and pragmatic approach based on the utility theory is presented. Finally, the

RRM framework for RENs is derived and the QoS-aware RRM approach based on the

utility theory is adapted accordingly.

3.2 Classical View on Resource Management

Let us consider a cellular network with a BS operating on a resource pool (e.g. a set of

frequency sub-carriers), for which the elementary radio resources are indexed with .

The BS serves a set of active MSs (indexed with ). Without loss of generality it may

be assumed that each of the MSs has only one active transmission. In such case the

index corresponds also to the different transmissions and in total there are active

transmissions in the cell.

CHAPTER 3 RESOURCE MANAGEMENT IN 4G CELLULAR NETWORKS 27

Purpose of the RRM functionality is to divide the radio resources between

MSs (see Figure 3-1). In general, each of the MSs can be granted with access to a

share of each resource element . In specific case there may be restrictions to

the resource allocation, i.e. not every MS may be capable to take advantage of every

resource element . Such cases will be addressed in Section 3.4 with respect to RNs. For

relay-less networks (RLNs), however, the assumption of lack of restrictions to resource

allocation is typically valid.

Figure 3-1 Resource allocation problem in a traditional (relay-less) system

With respect to the capacity of the radio link of the MS on the resource

element the resource allocation scheme results in a certain

transmission data rate available for the MS. The relation between the resource

allocation and the MS’s data rate is:

subject to

(3.1)

It is assumed here that no more than 100% of each resource element can be

assigned. This corresponds to a single-user single-stream MIMO transmission

scheme [13]. If advanced MIMO techniques are utilized in the system, more than 100%

allocation could be potentially used depending on the diversity level (rank) of the

transmission channels. However, even in the multi-stream MIMO case the resource

allocation can be normalized to fulfil formula (3.1).

Resource assignment formula (3.1) can be related to either instantaneous or long

term resource allocation. When considering instantaneous resource allocation, i.e.

resource allocation for a single transmission time interval (TTI), the resources are

assigned in a discrete manner, i.e.:

(3.2)


On the other hand, when formula (3.1) is used to describe the long term resource

allocation in a system, the resource shares allocated to MSs may take fractional values

according to the following definition:

(3.3)

The fractional resource allocation values correspond to averaging the resource allocation

in time domain, i.e. over multiple iteration of the resource allocation process. Further in

this work both notations are used, however, at each time it is directly stated whether the

description corresponds to a single TTI or to an average resource allocation process.

When considering allocation of resources to MSs in a single RRM

iteration, possible solutions exist. Let us have to denote the set of all possible

resource allocation schemes. Selection of one of the allocation schemes by the RRM

functionality depends on the assumed RRM policy. The policies are defined by the

performance indicators they aim at maximize. Next a few most common RRM policies

are described.

The historically basic RRM approach is the use of the utilitarian principle, i.e.

selection of the allocation pattern that maximizes the overall system performance [34].

The principle is defined with respect to the assumed notation by the following formula:

(3.4)

where is the utilitarian, i.e. system optimal, solution to the resource allocation

problem.

The utilitarian policy is often referred to as the best effort (BE) RRM approach.

This is because it prioritizes the MSs that can achieve the highest performance, thus,

providing the highest gain to the overall system performance:

(3.5)

The drawback of the BE policy is that it prioritizes MSs with good radio

conditions (e.g. cell-centre MSs) while not allocating any resources to MSs with poor

radio conditions (e.g. cell-edge MSs). This leads to high unfairness of performance in

the network. From the user perspective the high variation of achievable performance

with respect to location may indicate low reliability of connection and lead to low

overall perception of the network performance (even though the system performance is

maximized).


It was an effort of many investigations to provide solutions improving the users’

quality of experience (QoE) satisfaction. Commonly the QoE is related to the so called

ubiquity or fairness of performance in the network. To assess fairness of MSs’

performance various fairness metrics were defined. A comprehensive analysis of those

metrics can be found in the work of Dianati et al. [40]. The author lists three most

common fairness indicators. The fairness indicators are:

Max-min fairness (MMF) index [33]:

(3.6)

Jain fairness index [64]:

(3.7)

Gini fairness index [51]:

(3.8)

All three of the mentioned fairness indexes are: (1) normalized, (2) monotonic

and (3) scale invariant. The normalization feature guarantees all the values of the

indexes to be in the 0..1 range, with 1 indicating a set of equal values. The monotonic

feature enables comparison of different sets of values with respect to their fairness

(higher index value indicates higher fairness). The scale invariance feature provides that

the value of the fairness index does not change if all elements of the analysed set are

multiplied by a constant scaling factor , i.e. the following feature is retained:

(3.9)

The Gini index is commonly used in economic statistics, while in

telecommunication studies the max-min and Jain fairness indexes are more common.

The MMF index analyses fairness of a distribution based just on two extreme values of a

data set, and is independent of its intermediate values. The Jain index, on the other hand,

is based on the normalized variance of the values, thus, better reflects distribution of all

values in the data set. For this reason the Jain fairness index is used further in this work

for assessment of fairness of RRM algorithms.

The highest level of performance fairness is provided by another classical RRM

policy – the max-min fair approach. The MMF approach follows the Rawls’ theory of

justice [87]. According to this policy a resource distribution is optimal when one’s


wealth cannot be increased without decreasing wealth of another already poorer (or

rather not wealthier) being. With respect to the RRM this translates into maximization of

the lowest single user data rate and can be formulated as:

(3.10)

where is the max-min fair, i.e. fairness optimal, solution to the resource allocation

problem. Practical implementations of the MMF policy are commonly based on iterative

water filling algorithm, e.g. as described in [95, 97].

In contrast to the BE policy, the MMF policy prioritizes MSs with the poorest

channel quality. In general this has a negative impact on the overall system performance.

A trade-off between the BE and MMF policies is the approach proposed by Nash

in [82]. Nash’s approach is optimal with respect to the resource exchange process. The

optimality requires that it is not possible to transfer resources from one being to another

with positive sum of relative changes in their wealth. For such a case the following

inequality holds:

(3.11)

where is the proportional fair (PF) solution to the resource allocation problem.

The Nash’s theory of justice is the basis for the proportional fair RRM policy.

Principle of the PF approach is maximization of a metric calculated as the ratio between

the available performance gain for an MS per resource element and the MS’s already

achieved performance related to the previous iterations of the RRM algorithm. The PF

resource allocation metric can be defined as:

subject to

(3.12)

where is a parameter controlling level of fairness of the RRM. With the value close

to 1, the PF algorithm maximizes the potential performance with negligible impact of

the historical performance, thus, behaves similarly to the BE approach:

(3.13)


On the other hand, if is close to 0, the PF algorithm equalizes the historical

performance, thus, behaves similarly to the MMF approach:

(3.14)

The MMF and PF policies provide increase in the performance fairness

compared to the BE approach. However, they achieve it at the cost of the overall system

performance. To assess the impact of an RRM policy on the overall system performance

it is proposed in [34] to use the price of fairness (POF) metric. The POF is defined as the

relative decrease in the overall system performance, related to a resource allocation

scheme , as defined by the following formula:

(3.15)

Reference for the POF is the highest available system performance, i.e. the

performance available with the BE policy. In the further parts of this dissertation the

POF metric will be used aside with the Jain fairness index to assess quality of the

proposed RRM algorithms.

3.3 QoS-Aware Resource Management

The classical RRM policies focus on maximization of a performance metric related to

the transmission data rates achieved by MSs. Considering modern networks under

heterogeneous traffic conditions optimization based on just the achieved data rates is

insufficient. As explained in Section 3.1, various traffic types may have QoS

requirements specified in form of the preferred data rates and maximum packet delivery

times. To perform RRM optimization with respect to the QoS requirements the utility

theory can be used [47].

The purpose of the utility based RRM is, similarly as with the classical RRM

approaches, maximization of a certain key performance indicator (KPI). However, the

utility theory provides that the KPI may reflect a different set of preferences for each

MS. A joint RRM optimization can be performed with respect to the QoS requirements,

provided that all the requirements are translated to a common quantitative dimension

based on individual utility functions. This way it is possible to mathematically describe

and optimize even subjective factors impacting users’ satisfaction.

In this section, firstly, the general framework is derived for the utility theory

based RRM. Secondly, proposals of utility functions for the elastic and non-elastic

traffic are given.


The discussion presented in this section is partially based on state of the art

concepts. The author's input in this field was to collect and organize the most relevant

elements of the framework and fill in its missing elements. This includes proposal of

realistic utility functions for various traffic types. The presented framework of utility-

based RRM is extended by the author in Section 3.4.2 for a multi-hop relaying network

topology, which is an original addition to the concept.

3.3.1 Introduction to the Utility Theory

The system optimization problem with respect to the utility theory is finding the

resource allocation scheme that maximizes the cumulated utility of all the MSs in the

system:

subject to

(3.16)

This approach follows the classical best effort (BE) policy. The difference is, however,

that the definition of MSs’ utility functions has a deterministic impact on the outcome of

the optimization process.

A practical method for solving the constrained maximization problem, as defined

by formula (3.16), is the Lagrange multipliers approach. According to this method, if

there is a solution that maximizes the cumulated system utility, it is a stationary point

of the Lagrange function. The Lagrange function for the optimization problem (3.16) is:

(3.17)

where and are the Lagrange multipliers. Equation (3.17) includes also the

parameter that balances the resource allocation constraint, i.e. corresponds to

unallocated resources of the system.

To solve the Lagrange function it is required to find its stationary points, i.e. the

set of arguments for which all the partial derivatives of the Lagrange


function are not positive at the boundary points of the function domain. Partial

derivatives of Lagrange function (3.17) are:

(3.18)

(3.19)

(3.20)

(3.21)

(3.22)

Formulas (3.21) and (3.22) correspond to the already known constraints included

in formula (3.16), however, the remaining three derivatives (3.18), (3.19) and (3.20)

provide additional information on . Considering that the resource allocation is always

nonnegative ( and for all and ) and that transmission data rates are also

nonnegative ( for all ), the following set of conditions can be defined [67]:

(3.23)

(3.24)

(3.25)

where

is the marginal utility [90] of the MS with respect to the user’s historical

data rate .

The following conclusions can be drawn out of the above conditions:

is the marginal cost of utility [90] for MS , i.e. the price of changing utility of

the MS. According to formula (3.23), an MS can achieve increase in utility up to

the point that equalizes its marginal cost. If an MS’s marginal cost is higher than


the MS’s marginal utility at the lowest performance (i.e.

), the MS is

not assigned with any resources at all.

is the cost of allocated resources which translates to the MS specific marginal

cost of utility with the exchange rate of (capacity of the MS’s radio link

on the elemental resource ).

If the solution to the optimization problem is achieved at fractional system load

(i.e. not all resources are assigned, i.e. an exists such that ), the cost of

resources and MSs’ marginal costs of utilities are zero. This means that the

optimal resource allocation at fractional load can be achieved only if utility

functions of all MSs saturate at some data rate level (i.e.

).

Further solution steps depend on the definitions of the utility functions. A unique

solution that satisfies the above listed constraints exists if the utility functions are

strictly concave [46, 67]. Otherwise, there might not be a single solution to the

optimization problem.

A practical solution to the RRM optimization with respect to the utility functions

is the iterative resource allocation procedure described in Table 3-2. The iterative

resource allocation approach provides a close-to-optimum configuration even if the

single optimal solution does not exist. It also enables adaptation of the RRM

configuration to changing network conditions. The algorithm performs resource

allocation based on the priority metric defined as:

(3.26)

Table 3-2 Utility-optimal resource allocation procedure

1. Calculate the resource allocation priority metric for every active MS and for every

resource element accessible for the MS. Base the calculations on the historical statistics of

the performance achieved by the MSs (e.g. average achieved data rate).

2. For every resource element , assign this resource element to the MS , which has the

highest value of the priority metric . If multiple MS have the same value of the priority

metric , assign the resource element to a random one of those MSs.

3. Update MSs' performance statistics and repeat steps 1-2 in the next RRM iteration (i.e. the

next TTI).

The optimization procedure described above provides a system optimal solution,

i.e. following the best effort (BE) policy. Similarly as for the classical RRM approaches,

the utility theory also supports “fair” solutions to the resource allocation problem.


Specifically, there is the -fair [69] approach. The -fairness defines a utility translation

function for optimization with respect to a fairness parameter :

(3.27)

with the -fairness corresponds to the classical proportional fair (PF) approach.

To find an -fair solution to the resource allocation problem formula (3.16)

should be solved with utility functions recalculated according to -utility function

(3.27). Following the same solution steps as described above, the -fair priority metric

for the iterative RRM algorithm is derived as:

(3.28)

3.3.2 Proposals of Utility Functions

An important element of the RRM optimization based on the utility theory is appropriate

definition of the utility functions. In this section a set of utility functions is proposed

considering characteristics of various traffic types and the QoS requirements defined

earlier in Section 3.1.

Elastic Traffic

In case of the elastic traffic (ET) the user satisfaction criteria is the achieved data rate.

The higher the data rate, the higher the satisfaction. In some literature (e.g. [46, 66, 67])

it is proposed that the utility function for the elastic traffic should be strictly increasing

and concave. The concavity characteristic provides that there is a unique solution to the

system optimal resource allocation problem, and thus it is generally convenient to be

assumed.

The concavity of utility functions relates to risk aversion of the RRM process.

The risk aversion means that the expected marginal utility of a random process with a

zero expected outcome is always negative, i.e. the following condition holds:

(3.29)

With respect to the elastic traffic the risk aversion is, however, not in line with

the common sense. Specifically, if there is a portion of data to be transmitted to/from a

user, the overall satisfaction of the user from the service will depend on the overall

transmission time rather than the instantaneous data rates. Therefore, any variation in the

instantaneous data rates for the transmission should not impact the overall value of the


utility function as long as it does not change the average data rate. Following this

reasoning it is proposed here to define the utility function for the elastic traffic as a

linear function of the average data rate:

subject to (3.30)

Advantage of the proposed definition of the utility function for the elastic traffic

is that it makes the system optimal and the -fair RRM solutions converge with to the

classical best effort and proportional fair RRM policies. This makes the utility theory-

based RRM a natural extension of the classical RRM concepts.

Non-Elastic Traffic with Data Rate Requirements

If a communication service has the guaranteed bit-rate (GBR) requirement specified

(hereafter denoted as ), QoS satisfaction for this service is close to zero for the data

rates lower than the GBR and saturates at the maximum satisfaction level for data rates

higher than the GBR. To model such behaviour sigmoid-type functions are proposed in

the literature [46, 107]. In this work a parameterized logistic function is proposed as the

utility function for the GBR-bounded traffic. The GBR utility function is defined as:

subject to

(3.31)

with three configurable parameters: (1) priority of the service type , (2) parameter

related to the requested GBR and (3) parameter controlling steepness of the

utility function in the proximity of the GBR. Service priority weight determines

the maximum utility of the service type. Impact of the shape parameters and

is depicted in Figure 3-2.


Figure 3-2 Parameterization of the GBR utility function (wGBR

= 1)

Values of the three parameters , and are proposed here to be

determined based on three a priori assumptions:

Utility of a GBR traffic with satisfied GBR requirement is equal to

the utility of the ET at the data rate equal to the GBR level:

(3.32)

The maximum utility possible to be achieved can be higher than the utility at

precise GBR satisfaction. The additional utility corresponds to the increased

robustness of the transmission (packet error rate compensation, see Table 3-1):

subject to (3.33)

where is the maximum acceptable packet error rate defined for the QoS class

of the transmission.

There is a certain GBR satisfaction level for which value of the

utility function is . This point is the minimal operational GBR

satisfaction level for the service:

subject to (3.34)


Based on the three above defined assumptions, formulas for the three parameters

of the GBR utility function are:

subject to

;

(3.35)

Considering, e.g., a conversational video transmission with 2.5 Mbit/s GBR [28], target

packet error rate of 10-3

(see Table 3-1) and , the parameters of the utility

function are:

(3.36)

Figure 3-3 presents comparison of the elastic traffic utility function with the

utility functions of various GBR bounded traffic types. The comparison includes:

audio streaming with 320 kbit/s GBR [60],

standard definition (SD) IP television (IPTV) with 2.5 Mbit/s GBR [96],

high definition (HD) IPTV with 7.5 Mbit/s GBR [96].

Figure 3-3 Comparison of the ET and GBR utility functions


As already stated, a unique solution to the resource allocation problem exists if

utility functions are concave. In case of the GBR traffic the utility function is convex up

to and concave for higher data rates (see Figure 3-4). This indicates that the

system optimal resource allocation process for GBR-bounded traffic is characterized

with risk aversion as soon the utility value is achieved. Furthermore, if only the

RRM functionality is able to provide the utility for all MSs, a unique system

optimal resource allocation solution exists. The system optimal configuration can be

found using the iterative algorithm described in Table 3-2. Within the algorithm the

system optimal resource allocation for MS using a GBR-bounded traffic is done with

respect to the following best effort (BE) marginal cost of utility:

(3.37)

Figure 3-4 GBR utility function of a conversational video service


In case of the proportional fair (PF) resource allocation for GBR traffic the utility

function is strictly concave (see Figure 3-5), thus a unique stable solution always exists.

When using the iterative resource allocation algorithm from Table 3-2 the unique

proportional fair solution is found on the basis of the following marginal cost of utility:

(3.38)

Figure 3-5 Proportional fair GBR utility function of a conversational video service


Non-Elastic Traffic with Delay Requirements

Some non-elastic services may impose requirements on the maximum packet delivery

time (see packet delay budget, PDB, in Table 3-1). In case of such traffic, if a data

packet is not delivered to the target node within the predefined time, the packet is

dropped reducing QoS. To model impact of the packet latency on utility a modified

logistic function is proposed here to model utility related to the packet delivery time:

subject to

(3.39)

where is the utility function related to the expected packet delivery time ,

and are the maximum packet delivery time and packet size for the service, and

is a fixed shape parameter of the utility function. Shape of the PDB utility function

and its first derivative for an exemplary service type are depicted in Figure 3-6.

Figure 3-6 Delay-bounded utility function of a conversational video service

If a service is characterized with both the data rate and delay requirements, the

corresponding utility functions are combined as:

(3.40)


where is for the GBR-bounded traffic or for the elastic traffic.

Again, the resource allocation with the PDB-bounded traffic can be optimized by

using the method of Lagrange multipliers. By calculating the partial derivatives of the

Lagrange function, the marginal cost of utility is formulated generally as:

(3.41)

Considering the defined form of the PDB utility function (3.40), its marginal cost

of utility for the best effort (BE) RRM is:

(3.42)

and for the proportional fair (PF) RRM it is:

(3.43)

where the corresponding PDB marginal costs of utility are:

(3.44)

(3.45)

For both the best effort and the proportional fair resource allocation the marginal

cost of utility includes an offset related to the experienced delay in packet

delivery. Considering the properties of the logistic function used to define the PDB

utility, the PDB marginal cost of utility increases with the delay (see

Figure 3-7). This gives priority in scheduling for the transmissions with shorter available

times until packet drop (i.e. time to live, TTL). Taking into account Figure 3-7, for an

exemplary traffic with the maximum packet delivery time of 130 ms the scheduling

priority is meaningfully increased by the PDB marginal cost of utility if the remaining


packet TTL is below 10-13 ms (i.e. ~10% of ). This factor is controlled with the

parameter of the PDB utility function.

Figure 3-7 Price of utility for a GBR satisfied delay-bounded conversational video service

3.4 Resource Management for Relaying

Deployment of RNs in a cellular system adds new dimensions to the RRM procedures.

Specifically, three new aspects need to be considered:

1) a Type-1 DF RN is an access point that individually serves MSs within its

coverage region,

2) a BS serves not only MSs but also RNs and has to split resources between the

two types of nodes,

3) throughputs of the RN BH and AC links should be matched to avoid buffer over-

and underflows (as explained in Section 2.2.1).

Analysis of the above listed relay-enhanced network (REN) RRM relations is given

hereafter. Discussions presented in this section are partially based on the two of the

author’s publications [59] and [52] and extend the analysis included therein.

3.4.1 General Framework of Relaying RRM

Firstly, let us consider a two-hop REN as depicted in Figure 3-8. The network includes a

BS, a set of RNs connected to the BS, and a set of active MSs. There is a sub-set

of MSs connected to the BS and for each RN there is a sub-set of MSs connected to

this RN.


Figure 3-8 Two-hop relay-enhanced network model

In a network including two-hop relaying the RRM functionality may operate in

two layers. The first layer is the RRM at the BS. The second layer contains the RRM

functionalities at the individual RNs. In general, the RRM of the BS and RNs can be

considered as independent. However, with respect to the three aforementioned relaying

RRM relations, the BS and RN RRM layers should be self-aware and should cooperate

with each other for an efficient operation of the whole system. This is especially true if

network-wide performance fairness is expected to be provided for the BS- and RN-

connected MSs.

RN Resource Partitioning

The purpose of the RN RRM is to provide configuration for the RN access (AC) link,

i.e. for the nodes connected to the RN (MSs and, in case of multi-hop topology,

subordinate RNs). The RN access link configuration decided on by the RN RRM should

not come in conflict with the RN backhaul (BH) link configuration controlled by the

higher layer RRM (the BS RRM in case of two-hop relaying or the donor RN RRM in

case of multi-hop relaying). Specifically, an RN can assign to its own served MSs (and

subordinate RNs in case of multi-hop relaying) resources not assigned to its backhaul

link by its donor node. To avoid conflicts between the RN RRM and the BS RRM

decisions, resource reservation for the RN backhaul and access links ( and

respectively) is used. The resource reservation follows the DF RN resource partitioning

formula defined as:

(3.46)

The resource reservation for the RN backhaul and access links is not required if:

a fully centralized RRM scheme is used, i.e. the BS RRM in addition to its

baseline competence decides on configuration of RN access links, and

BS


RNs do not provide control information to the connected MSs.

The two features characterize the Type-2 relaying (see Section 2.2.1). In case of Type-1

relaying considered in this study the resource reservation is mandatory even if a fully

centralized RRM scheme is implemented. This is because the Type-1 RN has to

continuously provide control information to MSs (e.g. reference symbols) that would

interfere with the RN’s BH link without the strict resource partitioning.

According to the LTE-A system specification, reservation of resources for the

RN access and backhaul links is done (semi-)statically at the RRC protocol layer,

whereas, dynamic allocation of resources on the RN backhaul and access links with

respect to the instantaneous traffic and radio conditions is done dynamically at the MAC

layer by the packet scheduling (PS) functionalities of the BS and RNs. In the algorithms

described further in this dissertation an adaptive approach to RRM is considered for both

the RRC and MAC protocol layers.

Within the RRM freedom regime the RN PS can follow the same policies as

those available at the BS RRM. Specifically, the PS of the RN can assign a fraction

of its access link reserved resource pool to a specific MS connected to this RN as

described by the following formula:

subject to

(3.47)

where is the set of resources assigned to the MS by the RN . The resource

allocation to the MS follows a certain resource allocation policy. In this context the

single RN RRM is analogue to the RRM of a BS cell in a relay-less network.

BS RRM Operation in Relay-Enhanced Networks

Similarly as in a relay-less network, the BS RRM assigns resources to the nodes

connected to the BS. In relay-enhanced networks (RENs), however, the assignment is

done not only to MSs, but also to RN backhaul links. By using similar notation as in

formula (3.47) the BS resource allocation can be described statistically as:

subject to

(3.48)


where and

are the sets of resources assigned by the BS, respectively, to the MS

and the RN , is the resource allocation rate for the MS , and

is the resource

allocation rate for the RN with respect to the total system resource pool .

RN Backhaul/Access Capacity Balancing

The element linking the RN RRM and the BS RRM is the RN buffer management

process. As explained in Section 2.2.1, a DF RN cannot forward more data than it has

stored in its buffer and it cannot receive more data than it has space in its buffer. A

similar problem exists also in case of access points with wired backhaul link (i.e.

traditional BSs). However, typically it is true that the capacity of the wired backhaul is

significantly higher than the capacity of the radio interface of a BS and that the core

network can manage the BS buffer status. In case of relay-enhanced networks, each RN

provides information about the status of its buffer to its donor node, i.e. the buffer status

report (BSR) [7]. Based on the BSR, the donor node RRM may adapt the resource

allocation for the RN, i.e. decrease transmission rate if the buffer fill level exceeds a

certain threshold, or increase the transmission rate if the fill level is on average below a

certain threshold during an on-going transmission.

The RN buffer management creates the need for data rate balancing of the RN

backhaul and access link transmissions as described below with equation (3.49), and

further with equation (3.50) considering capacities of the two RN links. The balancing

does not have to be provided on every time instance, but as average in time (the

averaging window depends on the RN buffer size). Therefore:

(3.49)

(3.50)

where is the throughput of the backhaul link of the RN , is the throughput of the

MS connected to the RN , and

are capacities, respectively, of the RN

backhaul link and MS’s link to the RN on the resource element , is the factor of

the resource element allocation to the RN at the BS RRM, and is the factor of the

resource element allocation to the MS at the RN RRM.


Let us now introduce the average values for the channel capacities subject to a

certain resource allocation scheme:

for the RN link to BS (backhaul link of the RN ) averaged over the

resources assigned to the link, defined as:

(3.51)

for MS link to RN averaged over the resources assigned to the link,

defined as:

(3.52)

for the RN access link averaged over MSs connected to the RN, defined

as:

(3.53)

Formulas (3.51)-(3.53) are subject to the constraints:

(3.54)

where is the set of resources assigned to the MS on the access link of the RN , and

is the cumulative set of resources assigned on the access link of the RN to all the

MSs connected to this RN.

Considering the above defined capacities the RN backhaul/access balancing

formula (3.50) can be reformulated as:

(3.55)


This leads to definition of the relaying gain , i.e. the ratio between the average

capacities of the access and backhaul links of the RN :

(3.56)

The relaying gain indicates which of the RN links is weaker in terms of capacity

and requires more resources for the RN backhaul/access balancing.

Value of the relaying gain is a function of the average RN backhaul and

access capacities. As the averaging depends on the radio conditions measured by the

RN-connected MSs and the resource allocation to those MSs, the relaying gain is not

static. It is, however, possible to estimate the expected value for the relaying gain:

(3.57)

and reserve backhaul and access resources with respect to it:

(3.58)

Considering the maximum system resource utilization, the resource reservation

for the RN links is given by:

(3.59)

The instantaneous resource allocation to the backhaul link of the RN has to

simultaneously satisfy the resource partitioning criteria (3.59) and align to the BS RRM

policy (3.48). Considering the two criteria, the resource allocation to the RN backhaul

link can be formulated as:

(3.60)

The parameter included in the above formula is equal to if the bottleneck in data

flow over the RN is at the RN backhaul link. Otherwise, it is equal to

if the

bottleneck is at the RN access link.


Considering formula (3.60), the effective end-to-end capacity of the DF relay-

enhanced channel with respect to the average backhaul link capacity is given by:

(3.61)

whereas the effective end-to-end capacity of the DF relay-enhanced channel with respect

to the average RN access link capacity is determined by the formula:

(3.62)

By further derivations it is possible to define the effective end-to-end relay-

enhanced channel capacity for a specific RN-connected MS :

(3.63)

and the effective end-to-end capacity of a resource element assigned to the RN backhaul

link:

(3.64)

The effective end-to-end capacity of an MS’s link corresponds to the capacity of the

RN-connected MS’s link perceived from the BS perspective. Similarly, the effective

end-to-end capacity of a resource element assigned to the RN BH link corresponds to the

overall resource element capacity perceived by an RN-connected MS.

Data Multiplexing on RN Backhaul

The dependency between the RN RRM and the BS RRM manifests itself also in form of

multiplexing of data streams to the RN-connected MSs on the RN backhaul link. To

satisfy the flow continuity principle on per RN-connected MS level, data rates of the

individual MSs' transmissions included in the RN backhaul link transmission should

agree with the following formula:

subject to

(3.65)


where is the share of the RN backhaul link capacity assigned to the RN-connected

MS .

It is a matter of an a priori decision if multiplexing of the data streams is

controlled by the RN RRM, or by the BS RRM. In the LTE-A system implementation,

multiplexing of data streams on RN backhaul link is decided on by the donor of the

backhaul transmission, i.e. by the BS in case of a two-hop connection. With such

implementation, in case of a downlink transmission the RN receives multiplexed data

streams targeting different MSs and adapts packet scheduling with respect to the data

available in its buffer.

When the multiplexing of data streams on the RN backhaul link is decided on by

the BS, the multiplexing might be considered as an extension of the BS RRM. In such

case the BS RRM makes centralized decisions on resource assignment to the RN-

connected MSs. Thus, the resource assignment to the RN backhaul link is simply an

outcome of the resource allocation to individual MSs as described by the formula:

(3.66)

It should be, however, highlighted that in order to make a channel-aware resource

allocation fully centralized the BS would need to be provided with full system status

information. This includes, inter alia, information about capacities of all links and buffer

status reports (BSRs) of all connected nodes (RNs and MSs). Due to the control

information exchange overhead provision of such knowledge to the BS is typically not

effective. Therefore, decentralized RRM is commonly used in RENs, as described at the

beginning of this section.

To sum-up this part of the discussion, the resource allocation in an REN should

consider the following relations:

There is a limit on the maximum amount of RN backhaul and access link

resources that can be effectively utilized (see equation (3.59)). The limitation

results from the resource partitioning mechanism of the DF RNs.

Within the resource pool reserved for the RN access link, operation the RN RRM

can allocate resources to the RN-connected MSs following policies available for

BSs. The resource allocation is, however, bounded by the resource availability

for the specific MSs (see equation (3.47)). Resource allocation on the RN access

link depends in long term on the data stream multiplexing on the RN backhaul

link and vice versa (see equation (3.65)).

The BS RRM has to split resources between the BS-connected MSs and RNs

(see equation (3.48)). To achieve a certain resource allocation policy over the


whole system, i.e. for the BS- and RN-connected MSs, the BS should allocate

resources to the RN backhaul link with consideration of the performance and

requirements of the MSs connected to the RN (see equation (3.66)).

The above rules can be extended to a multi-hop REN scenario. Specifically, an

-hop relaying network can be considered as a combination of a finite number of

( )-hop networks, where each of the ( )-hop topologies is a branch of the

-hop relaying topology. This way, the -hop REN can be decomposed into a number

of two-hop sub-networks with subordinate RNs treated as MSs.

3.4.2 Extension of Utility Theory to Relaying

As stated earlier, one of the limitations of the existing relaying concepts is lack of

dedicated QoS provisioning mechanisms. In this section the utility-based QoS-aware

RRM concept described in Section 3.3 is extended over RENs. The concept extension

takes advantage of the relaying RRM relations defined in Section 3.4.1.

Let us start with the definition of the utility functions for the MS connected over

multi-hop links. As proposed in Section 3.3.2 the general utility function for any type of

service can be considered as a composition of the data rate dependent utility and the

packet delivery time related utility (see equation (3.40)). In case of an -hop

transmission the end-to-end data rate and packet transmission time

for the MS

satisfy the following relations:

(3.67)

where is the MS’s data rate at the th

component link counting from the source node,

and

is the packet transmission time at the MS’s last component link.

Taking advantage of the fact that the proposed utility functions are all strictly

monotonic, it is true that:

(3.68)

This means that in order to maximize utility of an RN-connected MS, utilities of the MS

at each component link need to be considered.

The end-to-end packet transmission time for a direct BS-MS link can be directly

estimated from the data packet size and the data rate of the MS’s transmissions (see

formula (3.39)). In case of a multi-hop connection also the number of component hops


needs to be considered. Therefore, for new data packets the end-to-end packet delivery

time can be defined as:

(3.69)

and for packets whose transmission has already started as:

(3.70)

where is the DF relaying forward time characteristic for a concrete RN

configuration, is the already past transmission time, and is the amount of data

available to the MS at its th component link.

Individual estimation of the packet delivery time done for each component link

gives the possibility to take into account the number of transmission hops of an end-to-

end BS-MS connection. By utilizing formula (3.69) the available time for transmission

at each component link is reduced in relation to the packet time to live (TTL)

proportionally to the number of transmission hops to the target node. This modification

is important for delay-sensitive traffic, as the basic packer delay budget (PDB) utility

function is unaware that the transmission might be a multi-step procedure.

Organization of the Relay-Enhanced Network RRM

When implementing a QoS-aware RRM for relay-enhanced networks (RENs) two

options are available: centralized and distributed management. The two approaches to

RRM in RENs with respect to the utility theory are depicted in Figure 3-9 and analysed

hereafter.


Figure 3-9 RRM schemes for multi-hop RENs: (a) centralized, and (b) distributed

In the centralized management scheme the BS RRM has the full knowledge of

utilities of all MS in its direct or indirect (i.e. relayed) coverage. Based on the

knowledge the BS RRM can decide on resource allocation to all links, i.e. direct RN

backhaul (BH) and indirect RN access (AC) links. With respect to the utility theory,

operation of the centralized QoS-aware RRM can be formulated as:

(3.71)

where is the end-to-end utility of the RN-connected MS , related to the resource

allocation scheme defining configuration of all the component links for this MS’s

-hop transmission.


A different approach is used with the distributed RRM scheme. Here the BS

RRM configures only the links of nodes directly connected to this BS (directly

connected MSs and directly connected RNs). Configuration of the relayed links is, on

the other hand, controlled by the RRM functionalities of RNs serving concrete

component links. Therefore, operation of the distributed utility-based RRM can be

formulated generally as:

(3.72)

where indexes the serving node (BS or RN) for which the RRM functionality is

considered, denotes the utility of the subordinate RN related to the resource

allocation scheme decided on by the RRM of the serving node . Analogically,

denotes the utility of the MS connected to the access point and related to the resource

allocation scheme . is the set of all resource allocation schemes available for the

RRM at the serving node , and is the set of subordinate RNs connected to the

serving node .

In the distributed scheme the RRM functionality of a donor node treats RNs in a

similar way as MSs. This requires defining the RN-specific utility function. To maintain

certain performance distribution over all MSs, the RN utility should be a function of

utilities of the RN-connected MSs. This allows performing resource allocation at each

level of the multi-hop topology independently and, at the same time, to maintain the

end-to-end QoS awareness for relayed transmissions. It is proposed here that the utility

function of an RN should be estimated as the sum of utilities of the nodes (MSs and

RNs) directly connected to this RN, as in:

(3.73)

Relay-Enhanced Network RRM Constraints and Optimization

For both the centralized and distributed management approaches to RRM in RENs the

following constraints apply:

The basic resource allocation conditions defined in formula (3.16) for a direct

BS-MS transmission. Those conditions are also applicable for RN-MS

connections.

The end-to-end data rate and packet delivery time formulas for a multi-hop

connection defined by formula (3.67) and the corresponding utility functions

defined by formula (3.68).


The resource partitioning principle defined by formula (3.59). With respect to the

utility theory the principle can be reformulated as:

(3.74)

where is the allocation factor of resource element to the BH link of the RN .

The buffer management principle defined as:

(3.75)

where is the transmission time interval (TTI) (1 ms in the LTE(-A) system), is

the data rate of the MS transmission originating from the RN , is the amount of

data targeting the MS and stored in the RN buffer, is the cumulated data rate on

the backhaul link of the RN , is the RN’s total buffer capacity,

is the

unused capacity of the buffer, and denotes an RN subordinate to the RN .

With respect to the above defined RRM constraints the resource allocation

priority metric for the centralized RRM scheme is:

(3.76)

where is the resource allocation priority metric for the MS and resource element

at the component link originating from the serving node (BS or RN), is the

truncated capacity of this link, and is the marginal cost of utility for the MS related

to the considered link.

Truncating of the link capacity relates to the data availability for the transmission

at the source node and the buffer space available at the target node. Limitations at any of

the two considered buffers restrict the effective capacity of the link as formulated in:

(3.77)

where is the real capacity of the link used for transmission to the MS at the donor

node on the resource element , is the available buffer space at the RN

subordinate to the serving node , and is the amount of data available for the MS at


the serving node . The effective channel capacity defined as above additionally takes

into account resource reservation for the access link of the serving node ( if

the serving node is the BS).

Considering the bottleneck effect formulated in equation (3.67), the marginal

cost of utility for the MS at the th component link is:

(3.78)

The above formula defines that the component links that are not the bottlenecks of the

relayed transmission are characterized with zero marginal cost of utility, i.e. the MS and

the system do not gain any utility if additional resources are assigned to those links.

Taking advantage of the full knowledge of buffer status at all the nodes in a

multi-hop connection, the centralized RRM can also make a prediction about the

expected packet delivery time for an on-going transmission as defined in formula (3.70).

This allows modifying the marginal cost of utility for a delay-bounded traffic so that the

time available for transmission at each component link is reduced by the estimated delay

of the further component links towards the target node.

On the basis of above defined formulas (3.76) to (3.78) the BS RRM can control

centrally resource allocation for all the component links of multi-hop connections. For

that, however, the BS needs to be provided with the knowledge about the quality of the

links and buffer status of all the nodes in the REN. Provisioning of this information to

the BS is often considered to be an excessive overhead.

The distributed RRM scheme requires significantly less information exchange. In

the distributed scheme every access point (BS and RNs) takes advantage of the system

status information that are by default available for it, i.e. channel quality and buffer

status of directly connected devices (MS and subordinate RNs). On the basis of this

information resource management is done at every access point in the same way, i.e.

resources are assigned with respect to the following priority metric:

(3.79)


where indicates a device (MS or a subordinate RN) connected to the serving node ,

is the truncated capacity of the link to the considered device calculated according to

formula (3.77), and is the marginal cost of utility for the considered device, which for

MSs is the same as in case of relay-less networks (see formula (3.26)), and for RNs is

calculated as:

(3.80)

where in index of the data packets stored in the buffer of the RN , is index of MS

being the target node for the packet , and is the number of hops between the BS and

the RN .

Again, knowing the topology of the network, i.e. knowing how many

transmissions hops there are between the BS and every RN, an RN can estimate how

many transmission hops a data packet will undergo to reach the target MS. This allows

taking into account the packet delay budget requirement more accurately for the multi-

hop RN-connected MSs.

3.5 Summary

This chapter presents description of a framework for resource management in modern

cellular systems. The framework is formulated firstly for relay-less networks (RLNs)

with respect to classical methods of resource management. The classical RRM policies

allow optimal network management in case of homogeneous traffic conditions, however,

cannot be applied directly in case of services with various QoS requirements specified.

A more universal RRM optimization method is derived next out of the utility theory. On

this basis an iterative resource allocation procedure is described that can be applied to

various traffic scenarios. For this purpose proposals of utility functions considering

various QoS requirements of common communication services have been also given.

The discussion is focused next on the relay-enhanced network (REN) specific

elements of resource management. In this context detailed analysis of RRM relations

between various aspects of REN operation is conducted. The main contribution of this

part of the dissertation is formulation of a set of guidelines for an efficient REN

operation. The guidelines are formulated in a generic manner, and, thus, are applicable

to any system supporting RENs based on the DF RN mode of operation. One REN-

specific RRM problem that is considered is elimination of bottlenecks on multi-hop

connections. With respect to this problem, criteria for RN BH/AC resource partitioning

are defined on the basis of throughput balancing for the two links and on the RN buffer

management process. The second REN-specific problem that is considered is division of

resources between MSs and RNs connected to the same serving node. To solve this task

it is proposed to treat RNs in similar fashion as MSs, but with cumulated traffic demands


of the MSs and RNs they serve. This way an overall fair resource allocation can be

provided for all MSs, disregarding the type of their direct serving node.

Analysis of the REN-specific RRM problems leads to extension of the utility-

based RRM concept over multi-hop relaying transmissions. Lack of functional QoS-

aware RRM solutions for relaying is the major factor limiting evolution and practical

application of this technique. Therefore, extension of the utility-based RRM concept on

RENs is a valuable input to the state of the art concepts. In this context two approaches

are formulated: centralized and distributed RRM. The centralized RRM considers that

the BS RRM functionality has the full knowledge about the system status and can decide

about configuration of all the links in the network. It is, however, burdened with high

signalling overhead. In contrast, the distributed RRM approach considers a multi-level

control procedure. It is based on decomposition of the system resource allocation

problem into smaller sub-problems specific for each branch of the relaying topology.

After such decomposition, each of the RRM optimization sub-problems can be solved

using adapted RLN management procedure.

59

Chapter 4 Single- and Multi-Carrier Relaying Schemes

4.1 Introduction

A single-carrier (SC) system configuration is the minimum spectrum arrangement

required for a radiocommunication system to operate. This includes support for the user

and control plane transmissions in both downlink and uplink directions. In case of

systems based on the time division duplex (TDD) the single-carrier configuration

corresponds to a single, continuous section of a radio frequency band, in which

downlink and uplink transmissions are multiplexed in time. In case of systems based on

the frequency division duplex (FDD) the single-carrier configuration corresponds to two

linked sections of radio spectrum, one for downlink and one for uplink transmissions.

In the framework of the IMT-A systems evolution it is proposed to enable

bandwidth extension via multi-carrier (MC) operation. The multi-carrier operation

scheme corresponds to a system with multiple component carriers (CCs) enabled. A CC

may be either a standalone backwards compatible carrier or an extension carrier. The

extension carrier is a CC supporting just user plane and not control plane communication

for increased spectral efficiency of the system for user plane data transmissions [14].

The CCs in a multi-carrier system may be either adjacent to each other or discontinuous

within one frequency band, or allocated in different frequency bands. This enables

exploitation of scattered portions of spectrum as part of the so-called frequency re-

farming or frequency white-space management [75, 83].

Although the multi-carrier operation might be seen as a simple bandwidth

extension of the single-carrier operation, it brings new opportunities and new challenges

to RRM. Especially, if CCs are allocated in different frequency bands, significant

discrepancies in radio propagation conditions between the CCs may occur. Furthermore,

the multi-carrier spectrum arrangement provides strict fragmentation of system resources

(i.e. on CC basis) which requires development of new carrier-based RRM procedures.

This chapter presents characterization and comparison of the single- and multi-

carrier configurations for relay-enhanced networks (RENs). The discussion includes

such elements as: system limitations to resource management, support of multi-hop

relaying and relaying performance in terms of QoS provisioning in the presence of

heterogeneous traffic conditions. In Section 4.2 and Section 4.3 the single- and multi-

carrier REN configurations are discussed, respectively, with consideration of the RRM

efficiency and fairness criteria. To the best of the authors knowledge analysis of RN

configurations under such criteria is not presented elsewhere in the state of the art

literature. Therefore, the presented discussion provides a new viewpoint on this topic.

60 CHAPTER 4 SINGLE- AND MULTI-CARRIER RELAYING SCHEMES

In Section 4.4 timing characteristics of multi-hop transmissions over single- and

multi-carrier relaying configurations are analysed. This allows estimating performance

of relayed links with respect to real time traffic services. The argument of additional

delays related to multi-hop communication is commonly considered in discussion on

relaying. Concrete assessments of this problem corresponding to realistic scenarios are,

however, not common in the state of the art literature on this topic.

Finally, in Section 4.5 advantages and disadvantages of the single- and multi-

carrier configurations are put together. This allows deriving a recommendation on the

REN configuration for the next generation networks.

4.2 Single-Carrier Systems with Relaying

4.2.1 In-Band Resource Partitioning

The baseline RN resource partitioning scheme defined in the LTE Release 10 standard is

the single-carrier in-band operation [2]. In this configuration the RN backhaul (BH) and

access (AC) links are multiplexed in time, i.e. there are certain time sub-frames

dedicated for the RN backhaul link operation and the remaining ones are dedicated for

the RN access link operation.

In the downlink transmission direction the BS transmits data to the RN in the

backhaul sub-frames. The RN buffers the received data and next forwards it to MSs in

the access sub-frames. Analogous sequence takes place in the uplink transmission

direction. The in-band relaying operation is also called “half-duplex”. This is because

the RN never receives and transmits at the same time per transmission direction (but it

transmits on the uplink backhaul link, while receiving on the downlink backhaul link,

and it receives on the uplink access link, while transmitting on the downlink access link,

see Figure 4-1).

Figure 4-1 In-band relaying operation

As discussed by the author in [58] and [53], the time domain multiplexing

(TDM) of the RN backhaul and access links has several major drawbacks. Firstly, to

secure robustness of the backhaul/access multiplexing with respect to the BS-RN

synchronization errors and signal propagation times over the radio interface,

transmission gaps are introduced at the backhaul/access switching moments (see

CHAPTER 4 SINGLE- AND MULTI-CARRIER RELAYING SCHEMES 61

Figure 4-2). With respect to the LTE system time frame configuration this means losing

at least 1 out of 14 transmission slots per sub-frame (or out of 12 slots in a highly

dispersive environment) [13], i.e. 7-8% capacity loss on the RN backhaul link [58].

Figure 4-2 LTE-A in-band backhaul/access multiplexing [58]

Fraction of the RN backhaul link capacity is also lost for the BS-RN control

information exchange (see Figure 4-3). It is estimated that the control information

overhead reduces the in-band RN backhaul link capacity for data transmissions by up to

4% depending on the number of RNs connected to a BS. Overall, it is estimated that the

capacity of the in-band RN backhaul link available for data transmissions is 7-12%

lower compared to the capacity of a direct BS-MS link with the same SINR [58].

Figure 4-3 LTE-A in-band relaying control information overhead [5, 59]

In the LTE-A relaying implementation the resource partitioning is done on the

basis of the multimedia broadcast over single frequency network (MBSFN)

mechanism [64, 13] The MBSFN mechanism defines that certain sub-frames (the

MBSFN sub-frames) are not used for the access link communication. During those sub-

frames MSs should not expect any signalling to be exchanged with their serving access

points. In the context of relaying, an in-band RN uses the MBSFN sub-frames to

suspend its access link operation. During the access link-disabled sub-frames the RN can

engage in the backhaul link communication with its donor node.

#0 #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 #0 ...

#0 #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 #0 ...

#0 #1 #0 ...Time liberated for backhaul link reception

BS transmission (1ms radio sub-frame)

BS transmission received by RN

#2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12

BS transmission symbols decoded by RN

Switching times

Lost symbol

Propagation delay

Control data

symbols

RN transmission in an MBSFN sub-frame

MS data channelsMS

control

channelsMS data channels

RN data channelsRN control ch.

Channels to

MSs and RNs

are multiplexed

in frequency by

means of packet

scheduling

BS transmission


For backwards compatibility reasons several restrictions apply to the MBSFN

configuration. The restrictions are [53]:

Maximum 6 out of 10 sub-frames in a radio frame can be configured as MBSFN

sub-frames, and thus used for the RN backhaul link operation. The MBSFN

restricted sub-frames are indexed as 0, 4, 5 and 9 in the 0..9 scale. The sub-

frames 0 and 5 are by default reserved for carrying signals for synchronization of

MSs to the network [15], while the sub-frames 4 and 9 are used by the paging

mechanism to inform MSs about incoming transmissions [1].

RN backhaul link communication should also support the HARQ procedure for

handling retransmissions [7]. Therefore, the MBSFN sub-frame configuration

needs to be done with 8 ms (i.e. 8 sub-frames) basic periodicity.

The LTE system uses 40 ms (i.e. 4 frames) periodicity for updating the systems

information that is broadcasted to MSs. This imposes that the minimal update

rate of MBSFN sub-frame configuration is 40 ms, as the system information also

informs MSs about the applied MBSFN configuration [8].

Based on the above listed restrictions the following MBSFN configuration

scheme is defined [53]:

There are eight basic MBSFN sub-frame assignments available and each of the

eight assignments has 8 ms basic periodicity.

The MBSFN sub-frames need to be skipped if they fall into the 0th

, 4th

, 5th

or 9th

sub-frame of a radio frame.

The eight basic MBSFN assignment options can be used individually or grouped,

thus allowing 28 – 1 = 255 different MBSFN configurations (the 256

th setting

corresponds to the case with no MBSFN sub-frames enabled).

With respect to the in-band relaying the MBSFN patterns can be characterized

with specific statistical properties that directly impact performance of relayed

transmissions. The most determinant statistic is the number of the backhaul-enabled sub-

frames as it directly impacts the maximum capacity of the relay backhaul link. Number

of the backhaul-enabled sub-frames should follow the backhaul/access capacity

balancing principle (see Section 3.4.1). In case of an RN with low backhaul link capacity

and high access link capacity (i.e. high relaying gain) high number of the backhaul-

enabled sub-frames should be used to increase the backhaul link transmission time.

Likewise, in case of an RN with high backhaul link capacity and low access link

capacity (i.e. low relaying gain) a low number of the backhaul-enabled sub-frames

should be used. The Impact of the number of the backhaul-enabled sub-frames on

performance of in-band relaying is investigated in detail further in Section 4.2.2.


Characterization of the in-band resource partitioning with just the

backhaul/access operation time share is insufficient as typically multiple MBSFN

patterns are available with the same number of the backhaul-enabled sub-frames.

Availability of the counterpart MBSFN patterns can be used, e.g., for time domain ICIC

as in [93] proposed by Bou Saleh et al. The counterpart patterns are, however,

characterized with different distributions of the backhaul-enabled sub-frames, which

may impact transmission times over the in-band RN (see Section 4.4).

4.2.2 RRM under In-Band Relaying Constraints

With respect to the sub-frame configuration described in the previous section the

resource allocation share for an in-band RN backhaul link operation can be configured in

range 0-60% with 7.5% resolution. This resource allocation should follow the

backhaul/access capacity balancing principle given by formula (3.59) defined earlier.

Furthermore, the same MBSFN restrictions apply to all in-band RNs, thus, usage of the

60% of the system resources that are backhaul-capable needs to be divided between all

RNs. At the same time 40% of the system resources are not accessible for operation of

the RN backhaul links. In case of low traffic load directly at the BS cell and high load at

RN cells, the backhaul-restricted resources may be unused, while the backhaul-capable

resources are overloaded.

The unavailability of certain system resources for the RN backhaul link operation

impacts performance of in-band relaying in two ways:

In-band RNs connected to a common donor node need to share the backhaul-

capable sub-frames. The resource sharing can be done in a soft way based on the

dynamic RRM of the donor node packet scheduling (PS) functionality. With

respect to the notation introduced in Chapter 3 this process can be formulated as:

(4.1)

where denotes all the MBSFN-capable resources, and is the set of resources

allocated to the RN backhaul link by the PS of the donor node (BS or superior RN).

For an in-band multi-hop connection an RN that is a donor node for subordinate

RNs has to resign from some of the backhaul-capable resources for operation of

the backhaul links of its subordinate RNs. This resource division needs to be

done on the resource reservation level controlled by the RRC protocol layer

according to:

(4.2)


The two resource sharing mechanisms limit resource availability for a single RN

backhaul link operation. This limitation, if manifesting itself, prohibits relay-enhanced

network (REN) to achieve the optimal resource allocation. The question to be answered

further in this section is, whether the single-carrier in-band resource partitioning

provides enough flexibility for an efficient and fair RRM? If it does not, then what are

the costs of the resource allocation restrictions?

Single Donor Node Resource Division

Let us consider a two-hop REN with RRM following a certain resource allocation

policy. With respect to this policy a subset

of all BS’s resources is allocated to

the BS-connected MSs and a resource subset is allocated to the RNs connected to

this BS. The resource sets are defined, respectively, as:

(4.3)

(4.4)

subject to

(4.5)

For such an RRM operation fairness of resource allocation can be estimated as the Jain

index of the MSs’ allocated resource shares. In this case the Jain index is defined as:

(4.6)

where is the probability that an MS is connected to the BS and not to an RN,

and is the share of the system resources allocated to all RNs in the set defined as:

(4.7)

If the resource allocation policy used in this REN is the proportional fair (PF)

policy and there are no specific resource allocation constraints, the long term average of

the RRM process results in a resource fair allocation, and the following equation is true:

(4.8)


where is the set of resources allocated to the MS according to the PF policy.

Now, if this REN is operated on a single-carrier and the RNs use the in-band

resource partitioning, the MBSFN configuration imposes limitation on the maximum

resource allocation to the RNs: . In such case, if the amount of resources that

should be assigned to the RNs according to the assumed RRM policy is higher than 60%

of all the system resources, the optimal resource allocation with respect to this RRM

policy cannot be achieved. Figure 4-4 depicts the impact of the MBSFN resource

allocation congestion on the resource allocation fairness for the PF RRM policy

calculated according to formula (4.6).

Figure 4-4 Jain fairness index of resource allocation for RN- and BS-connected MSs:

(a) full dynamic range, (b) results for PF RRM


The restriction on the maximum number of MBSFN sub-frames is not related to

the number of RNs in the REN. However, when considering a typical configuration of

an REN [2, 28] it is unlikely for a single RN to reach the 60% resource allocation limit.

For this to happen, according to formula (4.7), such an RN would need to serve at least

60% of the total REN’s traffic. On the other hand, assuming a certain MS-to-RN

connection probability it is possible to estimate how many RNs can be deployed in an

REN before the resource allocation congestion is reached. If MSs are deployed

uniformly in the BS cell and all the RNs have the same coverage, the allocation of MSs

to the BS and RNs is characterized by the binomial distribution. In such case the

probability of reaching the in-band resource allocation congestion is:

subject to

(4.9)

where is the probability that an MS is connected to an RN, is the number of

MSs in the BS cell, and is the number of MSs in the BS cell connected to RNs.

According to the analysis presented by the author in [59], in the 3GPP LTE sub-

urban test scenario (see Appendix A for details) a single RN supports on average 7% of

a BS sector coverage area (estimated on the basis of simulations of 20 random REN

realizations, each containing 21 BS cells and 210 RNs, i.e. 4200 RNs in 420 BS cells).

For this scenario, the probability of reaching the in-band resource allocation limitation is

depicted in Figure 4-5.

Figure 4-5 Probability of reaching the MBSFN congestion

with respect to the number of RNs in an LTE-A REN


On the basis of the presented data, the probability of reaching the MBSFN

congestion is negligible for up to 7 RNs per BS sector. In the case of 10 RNs per BS

sector, as defined in the 3GPP test scenario [2], this probability reaches 98%, thus,

decrease of the resource allocation fairness is almost certain.

The theoretical prediction of impact of the MBSFN congestion on the

proportional fair (PF) RRM fairness for two-hop relaying is also verified via LTE-A

REN system simulations (uniform network deployment with full buffer traffic model,

see Appendix A for details on the simulation methodology, including discussion of

reliability of the collected results given in Appendix A.5). Results of those simulations

(see Figure 4-6) show that in an REN with 7 RNs per BS sector RN-connected MSs are

assigned on average with 4% less resources than in the PF-optimal state. The unused

resources are distributed between BS-connected MSs. This discrepancy leads to decrease

of the Jain index of resource allocation per MS. This effect increases as the number of

RNs per BS sector increases and the MBSFN congestion is more probable.


Figure 4-6 Simulated resource allocation statistics of a two-hop in-band REN:

(a) expected allocation per MS, (b) Jain index over all MSs

In-Band Multi-Hop Relaying

If a multi-hop topology is established in a single-carrier network, RNs with in-band

resource partitioning need to coordinate MBSFN configurations of consecutive

component links. In practice this means that an RN acting as the donor node for a

subordinate RN cannot occupy all the backhaul-capable sub-frames for its own backhaul

link operation. The donor RN has to donate a certain number of the backhaul-capable

sub-frames to its subordinate RNs so that the following flow equation is satisfied:


subject to

(4.10)

If the donor RN used all the MBSFN sub-frames ( ), the subordinate

RN would not have any sub-frames left available for its backhaul link operation.

Therefore, a division of the MBSFN sub-frames is needed between the donor RN and

the subordinate RN(s). This split should consider the resource portioning relation for the

RNs as defined in equation (3.59) and the system’s resource allocation policy.

To analyse this mechanism, let us focus now on a sub-problem of the REN RRM

related just to a donor RN and its subordinate RN as depicted in Figure 4-7. The

donor RN is serving a set of MSs and the subordinate RN is serving a set of

MSs. For the purpose of this analysis it is irrelevant if the donor RN has a single

subordinate RN connected or multiple RNs. Therefore, the subordinate RN and MSs’

set will represent here the whole relaying sub-network served from the donor RN .

Figure 4-7 Multi-hop relaying sub-network concept

Let us assume that according to a certain RRM policy the donor RN is assigned

with a share of the whole system resources for its backhaul link operation that

corresponds to a certain data rate on the backhaul link of the RN . Following the same

RRM policy the RN backhaul link data rate is divided between the MSs and RN

connected to the donor RN in a certain proportion , so that:

(4.11)

where is the data rate of the RN , and is the cumulated data rate of the MS

group .


Considering the transmission flow equation (4.10) the relation between the

backhaul link data rate of the donor RN and the subordinate RN is:

(4.12)

and the corresponding relation between resource assignments for the two links is:

subject to

(4.13)

where is the fraction of all the system resources used by the backhaul link of the RN

, and is the ratio between the average backhaul link capacities of the RN and the

RN .

The division of data rates at the donor RN as defined with the above equations

corresponds at the BS RRM to the following effective resource allocation to the

individual RN-connected MSs:

(4.14)

The Jain fairness index for the resource allocation described with the above equations is:

(4.15)

which in time is asymptotic to:

(4.16)

If the used resource allocation policy is the proportional fair (PF) policy and

there are no specific resource allocation constraints, the long term average of the RRM

process results in a resource fair allocation scheme, and the following equation is true:

(4.17)


However, if the RNs are operated with the in-band resource partitioning scheme

the limitation on the total amount of MBSFN sub-frames may occur. For such a case the

share of system resources used by the backhaul link of the RN is defined as:

subject to

(4.18)

where is the share of the system resources that supports MBSFN operation, i.e.

60% for the LTE-A system. If the in-band resource partitioning limitation does manifest

itself, the allowed split of the donor RN data rate between the subordinate MSs and

RN is characterised with the following division factor:

(4.19)

Figure 4-8 depicts the maximum fairness of resource allocation available in the

considered in-band multi-hop relaying topology. The more resources is allocated to the

donor RN backhaul link, the less resources is available for the subordinate RN backhaul,

and the lower is the maximum resource allocation fairness. If low amount of resources is

assigned to the backhaul link of the donor RN (i.e. low ), the remaining MBSFN sub-

frames allow fair resource division between the donor RN-connected MSs and the MSs

connected to the subordinate RNs.

Figure 4-8 Jain fairness index of multi-hop in-band relaying (β = 1)


However, the fewer resources are assigned to the backhaul link of the donor RN

, the lower is the cumulative throughput of the sub-network of the RN . The price of

fairness (POF) at the data rate division is:

(4.20)

where is the maximum resource allocation to the backhaul link of the donor

RN , which enables throughput division at rate at the RN.

With respect to formula (4.18), the highest resource allocation to the backhaul

link of the donor RN , that enables throughput division at rate at the RN is:

(4.21)

Therefore, the price of fairness for a given fair throughput division rate is:

(4.22)

The above relation is depicted in Figure 4-9. If the subordinate RN does not

have any active MSs connected ( ), there are no conflicts related to the in-band

resource partitioning ( ). On the other hand, if there are no MSs connected

directly to the donor RN ( ), backhaul resources should be divided between the

backhaul link of the donor RN and the backhaul link of the subordinate RN in

proportion dependent on the capacity ratio of the two links ( ). In such case the

resultant performance of the multi-hop connected MSs is

times lower than

it could be if the MSs were connected directly to the donor RN (over a two-hop link).


Figure 4-9 Price of fairness of multi-hop in-band relaying

The simplest illustration of the POF-fairness trade-off is a two RN, three-hop

network. It is possible to assume that the two RNs are statistically identical, i.e., they

have the same expected traffic load per cell and have the same backhaul link capacities

( and ). In such case, depending on the RRM policy, the MSs connected to

those RNs would experience 33% average performance loss while preserving resource

allocation fairness, or 50% lower fairness while preserving cumulative performance, or a

trade-off of the two losses (see Figure 4-10 for ). Furthermore, if the donor RN

serves statistically identical subordinate RNs, the expected value of is . In such

case even higher fairness and/or POF decrease is observed (see Figure 4-10 for ).


Figure 4-10 Trading-off fairness and price of fairness of multi-hop in-band relaying (β = 1)

The conclusions derived above are also confirmed in LTE-A REN system

simulations (uniform network deployment with full buffer traffic model, see Appendix

A for details on the simulation methodology, including discussion of reliability of the

collected results given in Appendix A.5). As depicted in Figure 4-11 in the test scenario

the multi-hop in-band REN provides better average throughputs per MS than the two-

hop in-band REN only at dense RN deployments. The gain in average throughputs is,

however, insignificant (2%) and related loss of resource allocation fairness clear (-0.1 in

Jain index value).

Figure 4-11 Simulated mean throughput vs. fairness characteristics

of two-hop and multi-hop in-band relaying


On the basis of the presented analysis and simulation data it can be concluded

that the in-band resource partitioning is not an efficient configuration for multi-hop

relaying, nor for two-hop relaying deployments with high number of RNs per BS. When

using this configuration, the restriction on a common pool of BH resources for all RNs

results in POF increase and/or throughput fairness reduction compared to a

corresponding unrestricted RN configuration.

4.3 Multi-Carrier Systems with Relaying

4.3.1 Multi-Carrier Resource Partitioning

In systems based on multi-carrier spectrum arrangement RNs can be operated with

resource partitioning done in the frequency domain, i.e. out-band. The out-band resource

partitioning is based on allocation of disjunctive sets of component carriers (CCs) to the

access and backhaul links of an RN (see Figure 4-12). Depending on the spectrum

fragmentation into CCs, either one or more CCs can be assigned to each of the links.

This assignment can be done individually per RN to reflect the individual radio

conditions of each RN (i.e. its relaying gain). What is a significant difference with

respect to the in-band resource partitioning, is that all resources are available for both the

RN backhaul and access links. This allows frequency domain multiplexing (FDM) of

RNs on carriers, i.e. a carrier that is assigned to the backhaul link of one RN can be

assigned to the access link of another RN. Such resource allocation may increase

resource utilization efficiency, but it may also lead to generation of additional inter-RN

interference [54]. The inter-RN interference and its mitigation are discussed further in

this dissertation (see Section 5.3). In this section the efficiency of resource partitioning is

analysed for various relaying configurations.

Figure 4-12 Out-band RN configuration

The basic problem that occurs when considering the out-band resource

partitioning is its resolution. The split of radio resources between backhaul and access

links of an RN is done on per CC basis, and the CCs are defined statically per access

point. In multi-carrier relay-enhanced networks (RENs) the spectrum fragmentation into

CCs of a BS determines CC configuration for the RN backhaul links. CC configuration

used by an RN on its access link does not have to follow directly the spectrum

configuration of the BS, but has to be aligned to it. Specifically, if a bandwidth of a BS


CC is assigned to the access link of an RN, the RN can either use the whole allocated

spectrum as one CC or divide it further into smaller CCs (e.g. 20 MHz = 2x10 MHz).

Such refragmentation of spectrum into CCs may be desirable in some specific cases in

general, however, it is unnecessary. Further in this dissertation it is assumed that in an

REN one spectrum fragmentation into CCs is used by all access points (BSs and RNs).

The maximum bandwidth support specified for the LTE-A system to meet the

IMT-A requirements is 100 MHz divided into 5x20 MHz CCs [16]. With such a

configuration applied to a BS, the available resolution for out-band resource partitioning

is 20%, i.e. the backhaul link of an RN can utilize from 1 up to 4 out of 5 CCs.

Alternatively, the 100 MHz bandwidth could be achieved by further fragmentation of the

spectrum, e.g. as 10x10 MHz or 20x5 MHz, to provide higher resolution for the carrier-

based RRM procedures. This higher order spectrum division requires, however, more

complex transceivers [17] and may introduce additional system overheads (e.g. in form

of control channels, if the CCs are backwards compatible) [13]. Aggregation of more

than eight CCs per device is also, inter alia for the above mentioned reasons, not

supported in the existing LTE-A standard [15]. However, for the purpose of this study

the limitation on the spectrum fragmentation can be disregarded to predict out-band

relaying performance with beyond LTE-A multi-carrier system configurations.

The impact of resource partitioning resolution is estimated here on the basis of

the price of fairness (POF) performance indicator. Reference configuration for the POF

estimation is the optimal resource partitioning defined earlier with formula (3.59).

Depending if, as a result of the limited resource partitioning resolution, an RN is

assigned with less or more backhaul link resources than optimally, the RN is either

backhaul or access limited. The POF for the two cases is:

(4.23)

where is the relaying gain of an RN and is the amount of resources assigned to the

backhaul link of this RN relative to the total amount of system resources.

If the resource partitioning configuration for an RN is bounded with a resolution

of , the POF-optimal switching between configuration and should be done

at the RN relaying gain level fulfilling the following equation:

(4.24)


thus:

(4.25)

and the maximum POF related to the resource partitioning resolution is:

(4.26)

The above derived relations are depicted in Figure 4-13 for out-band resource

partitioning with 2, 5 and 10 CCs respectively. As a reference the POF of in-band

resource partitioning with the same total system bandwidth is also depicted. The POF of

the in-band resource partitioning includes 10% of the backhaul link capacity loss due to

TDM overheads as explained in Section 4.2.1.


Figure 4-13 POF of out-band resource partitioning

with respect to the number of system carriers

Although the in-band resource partitioning scheme is bounded with 10%

backhaul link capacity overhead, it provides better performance (lower POF) than the

out-band resource partitioning over two CCs for RNs with relaying gain below 0.81 and

above 1.25. This is the result of low resource partitioning resolution for the dual-carrier

out-band configuration ( = 0.5) and high resolution for the single-carrier in-band

configuration ( = 0.075). When the division of spectrum into carriers increases the

out-band configurations provide more gains over the single-carrier in-band

configuration. Specifically, if the number of system carriers is five, the out-band

configuration provides better performance for RNs with relaying gain close to 0.25, 0.66


and above 1.35. If the system bandwidth is divided into ten carriers the in-band

configuration loses the resolution advantage and the out-band configuration outperforms

it for most of the relaying gain values.

The low resolution of the out-band resource partitioning limits performance of

this configuration. On the other hand, the in-band configuration also has its limitations,

e.g. related to unavailability of 40% of resources for the backhaul link operation (see

Section 4.2.2). A trade-off between those two configurations is the so called hybrid RN

configuration, i.e. mixed in-/out-band resource partitioning, proposed by the author

in [58]. The hybrid RN configuration involves one in-band carriers and some out-band

backhaul and/or access carriers. Purpose of the in-band carrier is to provide high

resolution of resource partitioning and purpose of the out-band carriers is to provide

additional capacity to either of the RN links when the MBSFN saturation is reached.

Illustration of a hybrid RN configuration is depicted in Figure 4-14.

Figure 4-14 Hybrid RN configuration

Figure 4-15 depicts comparison of the POF of dual-carrier hybrid and out-band

configurations and a single-carrier in-band configuration using the same total system

bandwidth. The hybrid configuration uses an in-band carrier with an out-band access

carrier for relaying gains below 1.0, and an in-band carrier with an out-band backhaul

carrier for higher relaying gains. The presented results indicate that in a dual-carrier

scenario the hybrid configuration is the most effective one for RNs with the relaying

gains below 0.43. This high efficiency originates from the highest resource partitioning

resolution (3.75% as a result of 7.5% resolution in time and 50% resolution in

frequency). Next, in the relaying gain range from 0.43 up to 1.0 the time domain

resource partitioning of the hybrid configuration reaches MBSFN saturation and a pure

out-band configuration is used instead. Finally, for RNs with relaying gain higher than

1.0 an out-band backhaul carrier is used in addition to an in-band carrier, which provides

additional backhaul link capacity not affected by the TDM overheads. In addition, for

hybrid RNs with high relaying gain the time-domain resource partitioning used on one

of the carriers provides again high partitioning resolution leading to high efficiency of

the configuration.


Figure 4-15 POF of hybrid resource partitioning

The analysis presented in this section shows that efficiency of the out-band

resource partitioning schemes is low if the system spectrum is divided into a low number

of carriers. This low efficiency of resource partitioning may manifest itself as the

inability to provide an optimal resource allocation for RN’s backhaul and access links,

and as a result of frequent backhaul or access limitation. The in-band configuration is in

these terms characterized with significantly higher flexibility of resource partitioning. It

is, however, burdened with the limitation on the maximum amount of resources

available for RN backhaul operation and TDM overheads (see Section 4.2.2). A

trade-off between the two configuration is the hybrid configuration proposed by the

author in [58]. The hybrid RN configuration includes the advantage of high resolution of

resource partitioning of the in-band configuration with no resource allocation limitations

of the out-band configuration.

4.3.2 Inter-Carrier Self-Interference

In the out-band operation scheme the backhaul and access links of an RN are operated

on different component carriers (CCs), what provides protection from the RN self-

interference and enables full duplex operation. In practice, however, the protection from

the RN self-interference provided by frequency separation is not perfect and depends

strongly on two factors: quality of the RNs’, BSs’ and MSs’ transceivers, and frequency

offset between the backhaul and access CCs of an out-band RN. This problem is

discussed in this section.

Transmission (Tx) modules of all radio devices are characterized with a certain

out-of-band (OOB) power emission. This OOB emission is generally undesired, thus

standardization bodies such as the 3GPP or the ITU-R define restrictions on its


maximum levels. Those restrictions have the form of maximum spectral emission masks

or, for small frequency offsets, maximum adjacent channel leakage ratio (ACLR). On

the other hand, the reception (Rx) modules of radio devices are characterized with a

limited ability to filter out power from outside the desired frequency band. This inability

is characterized with the adjacent channel selectivity (ACS) parameter. Both the ACLR

and ACS parameters define OOB characteristics for relatively small frequency offsets,

i.e. adjacent or second adjacent CCs within one frequency band. If the Tx and Rx nodes

are operated with higher frequency separation, e.g. in different frequency bands, the

isolation between CCs can be generally considered as sufficient to protect from any

inter-carrier interference effects.

The 3GPP organization defines maximum values for the ACLR and ACS

parameters for various types of network devices, including MSs [18], BSs [19] and

RNs [20]. Values for those parameters for 10 MHz carriers are summarized in Table 4-1.

Table 4-1 3GPP standardized spectral transmitter/receiver characteristics [18-20]

BS MS RN

ACLR 45 dB 33 dB 45 dB

ACS 43.5 dB 33 dB 47 dB for BH

43.5 dB for AC

Considering a pair of Tx and Rx devices operated on adjacent carriers and

characterized, respectively, with the ACLR and ACS parameters it is possible to

estimate the level of inter-carrier interference coupling between those two devices. This

interference coupling is characterized with the adjacent channel interference ratio

(ACIR) calculated as:

(4.27)

On the basis of the values of the ACLR and ACS specified in Table 4-1 the self-

interference coupling ratio for out-band RNs ( ) can be estimated. For both

downlink and uplink transmission directions the value of this parameter is approximately

42 dB, i.e. the transmissions from the RN access are received at the RN backhaul with at

least 42 dB attenuation and vice versa. onsidering that the source of the RN’s backhaul

link signal (i.e. the donor node) is typically located far from the RN, and that the source

of the self-interference (i.e. RN access link transmitter) is typically collocated with the

RN backhaul receiver, the 42 dB of in-frequency isolation may be insufficient to

guarantee good quality of the RN backhaul link.


The RN backhaul link SINR degraded by the inter-carrier self-interference is:

(4.28)

thus, the ratio of the degraded out-band RN backhaul link SINR by to the SINR without

the self-interference ( ) can be estimated as:

(4.29)

where is the RN backhaul link SINR with perfect self-interference suppression,

is the power of the RN self-interference received by the RN on its backhaul link,

is the power received by the RN from its donor node on the backhaul link, is the

power transmitted by the RN on AC link, and is the additional backhaul/access

isolation that can be provided via antenna and/or receiver configuration, e.g. by

directional characteristics or spatial displacement of the backhaul and access antennas,

or interference cancelation.

The impact of the inter-carrier self-interference coupling on the out-band RN

backhaul link SINR according to formula (4.29) is depicted in Figure 4-16. The data

presented in this figure correspond to two basic 3GPP defined evaluation scenarios for

LTE-A RENs [2] (see also Appendix A for details of these scenarios).

Figure 4-16 Out-band RN BH link SINR degradation due to inter-carrier

self-interference coupling between adjacent carriers [58]


RN BH link SINR sensitivity to self-interference is significantly higher in the

3GPP sub-urban scenario (i.e. low density deployment) than in the dense urban scenario.

This is because in the sub-urban scenario distance between an RN and its donor BS is

significantly higher than in the dense urban scenario, and thus the BH link signal power

is lower. In both cases, however, the adjacent channel interference ratio (ACIR) of

42 dB is insufficient to provide full protection from the self-interference. To maintain a

relatively acceptable level of the RN backhaul link SINR degradation of up to 3 dB, the

RN would need to be provided with at least 50 dB of additional backhaul/access antenna

isolation (AI) in the sub-urban scenario and at least 38 dB AI in the dense urban

scenario.

As discussed above for out-band RNs and earlier in Section 4.2.1 for in-band

RNs, both types of resource partitioning have their overheads to backhaul link

performance. For in-band RNs those are the time-domain multiplexing and control

information overheads, and for out-band RNs this is the imperfect self-interference

protection and resource partitioning resolution. Figure 4-17 presents comparison of the

in-band and out-band backhaul link spectral efficiency in the typical radio conditions

corresponding to the 3GPP evaluation scenarios. The backhaul link spectral efficiency

data depicted in Figure 4-17 is calculated from the backhaul link SINR values depicted

in Figure 4-16 using the truncated Shannon function depicted in Appendix A, in Figure

A-1.

Figure 4-17 Comparison of in-band and out-band relaying performance

at the same radio conditions

For both scenarios (dense urban and sub-urban) a threshold antenna isolation

(AI) level can be identified above which the out-band resource partitioning provides

higher performance than the in-band configuration. For the dense urban scenario this


threshold AI is approximately 43 dB and for the sub-urban scenario this is 53 dB. For AI

values higher than the threshold AI the out-band provides up to 7-12% higher BH link

spectral efficiency as the in-band relaying overheads do not apply. However, if the

sufficient AI cannot be provided, the in-band resource partitioning should be used.

Considering recent development of advanced receivers and interference

cancellation schemes as described, e.g., in [24] it can be predicted that provisioning of

sufficient level of self-interference cancelation for out-band RNs should not be

problematic in the near future. Furthermore, with refarming of spectrum taking place it

can be predicted that system operation on carriers allocated in different frequency bands

will be common. At this stage application of multi-carrier configuration schemes will be

mandatory. At the same time the high frequency separation of the carriers allocated in

different frequency bands provides full protection from the RN self-interference, thus

the inter-carrier self-interference will not be a problem for out-band RNs. Taking this all

into account, further in this dissertation it is assumed that full self-interference

suppression is available for out-band RNs.

4.4 Transmission Delays over Relayed Links

Transmission on a radio link requires a certain time. This time involves packet

preparation for transmission, queuing time and transmission time. For relayed

transmissions the waiting times cumulate over all component links. The total sustained

delay might be unacceptable in case of some delay sensitive traffic types, e.g. voice over

IP (VoIP) or online gaming (see Table 3-1). Excessive transmission times would make

such services unavailable for the RN-connected users. Study of this problem is described

hereafter. It is extension of the author’s work presented in [53].

Single-Carrier Relaying Delays

A side effect of the in-band resource partitioning is delay related to half duplex

operation of RNs. The delay corresponds to unavailability of a specific sub-frame type

when there are data packets ready for transmission. Specifically, a data packet of a

downlink transmission needs to first wait at the source BS for a BH-enabled sub-frame

to be transmitted to the RN. Next, the data packet needs to wait for an AC-enabled sub-

frame to be forwarded to the target MS.


The in-band resource partitioning based on the MBSFN sub-frames can be

characterized in terms of the number of the BH-enabled sub-frames and their

distribution. The main impact of the number of BH-enabled sub-frames is on the

capacity of the RN backhaul link. This relation has been discussed in Section 4.2. In

terms of delay the number of BH-enabled sub-frames also determines the average

waiting time between two consecutive backhaul or access sub-frames. These two time

intervals can be defined respectively as:

(4.30)

(4.31)

where is the waiting time between two consecutive BH-enabled sub-frames, is

the waiting time between two consecutive AC-enabled sub-frames, and is the ratio

of the BH-enabled sub-frames number to all sub-frames defined as:

(4.32)

where is the set of BH-enabled sub-frames and is the set of all sub-frames in the

40 ms MBSFN period.

The number of the BH-enabled sub-frames defines the average values for the

and times. However, it does not give precise information on the exact delay that

should be expected for transmissions over an RN with a concrete MBSFN configuration.

This is because the LTE-A in-band resource partitioning scheme based on the MBSFN

sub-frames supports multiple configurations characterized with the same number of BH-

enabled sub-frames, but with various distribution of those sub-frames.

In the LTE-A in-band resource partitioning scheme there are 255 different

MBSFN configurations available in total (the configuration with zero BH-enabled sub-

frames is not considered here), but only 8 values are possible for the BH-to-all sub-

frames ratio . Furthermore, there are a number of MBSFN configuration patterns

that are statistically equivalent, i.e. they are identical with respect to certain time shifts.

Overall, out of the 255 MBSFN patterns there are only 31 uncorrelated patterns. Those

31 unique MBSFN patters are depicted in Figure 4-18.


Figure 4-18 Unique MBSFN sub-frame patterns for two-hop relaying [53]

By analysis of the MBSFN configuration patterns it is possible to estimate

additional characteristic times of the in-band resource partitioning. These times are:

The waiting time for the first BH-enabled sub-frame ( ), i.e., the minimum time

a downlink transmission data packet needs to wait at the BS before it can be sent

to the RN. The expected value for this time is:

(4.33)

The RN downlink forwarding time ( ), i.e., the minimum time a downlink

transmission data packet needs to wait at the RN before it can be forwarded

towards the target MS or a subordinate RN. The expected value for this time is:

(4.34)

Analogous times can be also defined for uplink transmissions.


Figure 4-19 depicts comparison of the in-band relaying characteristic times for

the 31 unique MBSFN configuration patterns. The general trend that can be observed is

that the and times decrease with increasing number of BH-enabled sub-frames

(i.e. increasing ). At the same time the time increases, but the change is not as

significant as for the three remaining times. The time is not depicted in Figure 4-19

as it is directly related to the time.

Figure 4-19 also illustrates variation of the characteristic times for MBSFN

patterns with different distributions of the BH-enabled sub-frames. Specifically, let us

consider the MBSFN patterns #3 and #5, both with . The pattern #5 defines

the BH-enabled sub-frames to be distributed in time, while the pattern #3 includes the

BH-enabled sub-frames to be grouped in blocks of two (see Figure 4-18). This results in

the and times to be higher for the pattern #3, respectively, by 29% and 33% than

for the pattern #5.


Figure 4-19 Characteristic times of unique MBSFN sub-frame patterns for two-hop relaying [53]


On the basis of the four characteristic times defined above it is possible to

estimate the expected total transmission time for a two-hop in-band relaying connection

( ). For a data packet of size this time is:

subject to

(4.35)

where is the minimum number of transmission TTIs required to deliver the data packet

over a direct link with data rate .

Estimations of the in-band delay overhead for various values of and are

depicted in Figure 4-20. The delay overhead is defined here as the ratio between the

expected end-to-end transmission time over two-hop in-band relayed link to the

expected time of a direct transmission with the same data rate and packet size. As can be

observed, the delay overhead is the highest for small values of . For such cases the

dominant components of are the fixed overhead times related to initialization of

transmission on a given link type ( and , respectively, for the backhaul and access

links in case of a downlink transmission). For transmissions characterized with high

values the delay overhead stabilizes at level dependent mainly on the and times,

related to the continuous flow of data on a given link type.

Figure 4-20 Expected in-band delay overhead for two-hop relayed links [53]

Estimation of the end-to-end transmission times over an in-band relaying link is

relatively simple for a two-hop connection. In case of a multi-hop in-band topology the


task is, however, not as straightforward. For a multi-hop connection various MBSFN

configurations should be considered for each involved RN, together with timing relation

between those MBSFN patterns. For this reason LTE-A system simulations are

conducted to assess the in-band multi-hop relaying delays. In the simulations four types

of delay-bounded traffic are considered: HD and SD IPTV, audio streaming and online

gaming (see Appendix A for modelling details, including discussion of reliability of the

collected results given in Appendix A.5). The simulations are conducted with 60%

average system load. This corresponds to a 20 MHz system with on average: 1 HD

IPTV, 5 SD IPTV, 25 audio streaming, and 25 online gaming MSs per BS sector

(29 Mbit/s cumulated guaranteed bit-rate, GBR, per BS sector). The assumption of

fractional load is undertaken to maximize probability of GBR requirement satisfaction

for all MSs and minimize impact of congestion on the delay analysis. Results of the

simulations are depicted in Figure 4-21.


Figure 4-21 Simulated transmission times for multi-hop in-band relaying with:

(a) HD IPTV, (b) SD IPTV, (c) audio streaming, and (d) online gaming traffic

The simulation results indicate that for RN-connected MSs with full GBR

satisfaction (lower end part of the CDFs in Figure 4-21 up to inflection points) relation

between the number of relaying hops and packet transmission time is linear. The

increase in packet transmission time for multi-hop connections is typically 4-5 ms per

relaying hop after the first hop. This factor is independent of the traffic type (i.e. data

rate and packet size) and relates directly to the in-band relaying delay.

Multi-Carrier Relaying Delays

With multi-carrier relaying the issue of increased packet transmission times is not as

critical as for single-carrier in-band relaying. The inter-carrier isolation of the RN access

and backhaul links enables full duplex operation. Specifically, both RN links can be


active at the same time, all the time. Therefore, a data packet can be forwarded by the

RN to the target node as soon as it is received from the source node.

In case of out-band relaying the full duplex operation is available on all

component carriers (CCs). In case of hybrid relaying one CC (the in-band

backhaul/access primary CC, PCC) is operated in the half duplex mode, while the

additional BH and/or AC secondary CCs (SCCs) are operated in the full duplex mode.

Therefore, at least fraction of the traffic transmitted over a hybrid RN is operated in the

full duplex mode, thus achieving lower overall delay compared to the baseline in-band

configuration. If the packet scheduling (PS) functionality of the hybrid RN is aware

which CC is configured for in-band operation, delay-oriented traffic steering can be

applied. Specifically, the PS of a hybrid RN can schedule data packets with short time to

live (TTL) for transmission on the full duplex out-band SCCs, while the data packets

with longer TTL can be transmitted on the half duplex in-band PCC.

Transmission times over out-band and hybrid multi-hop links are assessed next

on the basis of LTE-A system simulations. The simulated scenario is aligned with the

simulation scenario used earlier in this section for assessment of in-band relaying delays.

Figure 4-22 depicts comparison of delays experienced by RN-connected MSs in

case the RNs use in-band, out-band or hybrid configuration. Statistics for connections

with up to six component links are included. The first conclusion that can be made is

that the transmission times experienced with in-band configuration are significantly

higher than the times available with multi-carrier RN configurations. Secondly, the

transmission times for the hybrid configuration are higher than the times available with

the out-band configuration. The difference is, however, negligible for the online gaming

traffic with the lowest packet delay budget (PDB) requirement, and gradually increases

for traffic types with higher PDB settings. This trend corresponds to the traffic steering

feature of the hybrid RN PS. Specifically, packets of the online gaming service are

prioritized for transmission on the full duplex SCCs, while the IPTV packets are more

commonly transmitted on the half duplex PCC.


Figure 4-22 Simulated transmission times for various configurations of multi-hop relaying with:

(a) HD IPTV, (b) SD IPTV, (c) audio streaming, and (d) online gaming traffic

4.5 Summary

This chapter discusses REN operation with single- and multi-carrier spectrum

arrangement. For both scenarios appropriate RN configurations are described and

analysed. In systems operated on one carrier the only option available for RN resource

partitioning is time domain multiplexing, i.e. the in-band configuration. This is the

baseline configuration commonly considered for existing cellular networks. On the other

hand, if the system spectrum is arranged into multiple carriers, resource partitioning

schemes are available based on frequency or frequency and time domain multiplexing.

The configuration based on frequency domain multiplexing is the out-band

configuration defined in the state of the art specifications. The configuration based on


both time and frequency multiplexing is the so called hybrid configuration proposed by

the author in [58].

The analysis presented in this chapter shows that the baseline LTE-A single-

carrier in-band RN configuration is characterised with multiple resource management

restrictions that make its operation inefficient. Specifically, for backwards compatibility

with a legacy LTE system not all radio resources are available for RN backhaul link

operation. This leads to degradation of performance for RN-connected and/or loss of

RRM fairness. The inefficiency of single-carrier in-band RN operation manifests itself

especially in two scenarios: when the number of RNs served from a common donor node

is high (7 or more RNs per BS sector in a typical 3GPP evaluation scenario), or in case

of in-band multi-hop connection. The multi-carrier out-band and hybrid configurations

are not bounded with such restrictions, which enables more effective RN operation in

both two- and multi-hop topologies. On the other hand the out-band resource

partitioning scheme suffers from low resolution of resource assignment, which also in

some cases (for specific values of the relaying gain) may lead to inefficient operation.

This shortcoming of out-band resource partitioning scheme is, however, mitigated in the

hybrid scheme proposed by the author. The hybrid relaying scheme combines the best

characteristic of the in-band and out-band schemes, and at the same time lacks their

main drawbacks.

Secondly, timing analysis was conducted for all the considered RN

configurations. Again, the single-carrier in-band configuration shows its inefficiencies.

The time domain multiplexing of RN backhaul and access links used in the in-band

configuration leads to generation of additional delays in packet transmission. In case of

multi-hop connections the delays cumulate, which can make delay sensitive services not

available over such connections. The additional delays are not present with the

frequency domain resource partitioning used by the out-band RN configuration. For this

configuration only the minimal delay of RN processing time (1-2 ms per RN) is added to

the end-to-end packet transmission time. Also the hybrid configuration does not

introduce excessive delay in packet transmission. In this case, however, dedicated traffic

steering feature is required in packet scheduling.

The analysis presented in this chapter showed that relay-enhanced networks

(RENs) operated with multi-carrier spectrum arrangement typically achieve better

performance (or performance fairness) than RENs operated on a single carrier with the

same total spectrum bandwidth. The benefits of multi-carrier operation are available for

MSs using both elastic and real-time traffic. However, it should be noted that if division

of spectrum into component carriers is not possible, the in-band RN operation is the only

option available. The in-band operation is also recommended in a few specific cases, e.g.

when RN self-interference is strong in relation to the feeder link power.

95

Chapter 5 Carrier-Based RRM Coordination

5.1 Introduction

The previous chapters of this dissertation analysed resource management from the point

of view of MSs’ and RNs’ individual requirements. In this chapter a higher, i.e. system

level RRM is considered. The purpose of the system level RRM is to maximize the

overall network performance via coordination of configuration of access points. In case

of relay-enhanced networks (RENs) this involves coordination of BS configuration and

configuration of RNs’ backhaul (BH) and access (AC) links. The BS coordination is

already a well-studied problem (e.g. see the inter-cell interference coordination, ICIC,

concepts described in Section 2.3), thus it is not considered in this dissertation. The work

described hereafter concentrates, therefore, only on coordination of RNs’ configuration.

The two basic coordination problems in cellular systems are: interference

coordination and load balancing. The purpose of the interference coordination is to

increase the signal quality levels in the network, while the load balancing handles

congestion avoidance. In the context of RENs the two problems take the form of:

mitigation of BS-RN and RN-RN interference, and

securing sufficient resource availability for operation of RNs’ BH and A links.

Both problems are not specific for multi-carrier RENs and were studied earlier

for single-carrier RENs, e.g. as described by Bou Saleh et al. in [35, 93]. The single-

carrier REN operation imposes, however, restrictions on the RRM coordination related

to the TDM-based resource partitioning. Therefore benefits of the single-carrier REN

coordination are limited.

It is the purpose of this chapter to propose and analyse concepts of carrier based

coordination schemes for multi-carrier RENs. Firstly, the carrier-based load balancing

methods are described. This includes proactive methods for congestion avoidance, and

reactive methods for congestion resolution. Secondly, the interference coordination

scheme is proposed that includes inter-RN interference detection and resolution, with a

potential implementation on several decentralization levels.

5.2 Carrier-Based Load Balancing

5.2.1 Principles

Load balancing is a general name for a set of techniques for avoiding congestion at a

certain resource group (sub-group of the system’s resource pool) by reassigning some of

the MSs using those resources to another less loaded resource group. The congestion

state is defined as the inability to meet traffic demands of the MSs assigned to the

resource group (non-elastic traffic scenario), or as a significant disproportion of the

96 CHAPTER 5 CARRIER-BASED RRM COORDINATION

performance provided over the overloaded resource group in comparison to the

performance available at other resource groups (elastic traffic scenario).

In the non-elastic scenario each MSs is characterized with a certain requested

data rate and a channel capacity per resource group . As on each of the

resource groups this MS may observe a different channel capacity, a different amount of

resources may be required to satisfy its traffic request. This corresponds to a certain

traffic load potentially generated by the MS depending on the resource allocation. The

traffic load relative to the size of the resource group is defined as:

(5.1)

The target for the load balancing algorithm is to allocate MSs to resource groups

so to maximize the number of satisfied MSs. This might be considered as a variation of

the knapsack problem [37] with multiple knapsacks (resource groups) and items (MSs)

having different size (traffic load) depending on the knapsack they are being packed to

(allocation scheme). If each MS can occupy only one resource group at a time, this is the

0-1 knapsack problem, while if multiple resource groups can be aggregated per each

MSs, this is the fractional knapsack problem. For both scenarios solutions based on the

so called dynamic programming can be found in the literature [37]. The solutions are

however too time consuming for implementation with real time execution.

The other definition of load originates from the performance fairness criterion.

According to this definition a resource group is overloaded if the performance offered

for the MSs allocated to this resource group is significantly lower than the performance

at the other resource groups. According to this definition the load imbalance between

two resource groups is defined as the ratio of the expected data rates per served MS:

(5.2)

where is the set of MSs using the resource group . In this sense, the load balancing

procedure corresponds to reduction of variation of expected performance per resource

group by means of MSs allocation control.

Traditionally the load balancing technique is applied to network cells. If a cell in

a network is overloaded, it can handover some of its cell-edge MSs to an under-loaded

neighbour cell (see Figure 5-1). This way the overloaded cell has less traffic to handle

and the handed-over MSs can reach higher performance due to higher resource

CHAPTER 5 CARRIER-BASED RRM COORDINATION 97

availability (even though their signal quality typically decreases). This technique is

studied in detail by Stefański et al. in [65, 72, 73].

Figure 5-1 Inter-cell load balancing

In multi-carrier systems the load balancing may refer also to component carriers

(CCs). It is possible that one of the CCs of an access point (base station or a relay node)

is overloaded while other CCs are under-loaded. In such case the load balancing can be

done between CCs of one access point (see Figure 5-2). In such case it is possible to

increase resource availability per MS without decrease of their signal quality.

Figure 5-2 Intra-cell inter-carrier load balancing

In multi-carrier RENs the carrier load balancing (CLB) deals with:

decision on the number of CCs to be allocated to an MS or an RN BH link,

decision on which CCs to allocate to an MS or an RN BH link.

Both of the decisions can be done a priori with respect to predicted load generation of an

MS or an RN (proactive load balancing), or a posteriori as a reaction on load imbalance

detection (reactive load balancing). Concepts for the two load balancing approaches are

described next.

5.2.2 Proactive Load Balancing

Carrier Selection Schemes

The purpose of the proactive carrier selection-based load balancing is to allocate MSs

and RNs to CCs of an access point (BS or RN) in a way that minimizes congestion

probability on any of the CCs and maximizes performance. In the state of the art

literature this concept is explicitly studied by Wang et al. and described in [99-102]. In

Over-loaded cell Under-loaded cell

Inter-cell handover

for BS load balancing

Over-loaded CC

Under-loaded CC

Inter-frequency handover

for CC load balancing


this work Wang et al. focus on the carrier load balancing (CLB) in RLNs, i.e. allocation

of MSs to CCs of BSs. As those studies concentrate on MSs only, two important

simplifications are made. First of all, Wang et al. assume that all MSs are statistically

equivalent, i.e. they have the same characteristics of traffic requirements. Secondly,

Wang et al. assume that the MSs are in constant movement, thus they experience diverse

radio conditions with the same expected value of channel quality. Based on the two

assumptions all MSs generate the same expected load, thus the load balancing problem

is reduced to uniform distribution of MSs on the CCs of a BS. Additional implication of

the MSs’ mobility assumption is also dynamics in observed radio conditions per MS.

Specifically, the CC that provides the highest signal quality for a MS in one location can

provide worse conditions in other location (see Figure 5-3). Therefore, it is considered

that carrier selection for MSs should not be done in a channel aware manner to avoid

frequent inter-carrier handovers.

Figure 5-3 Dynamics of radio conditions per CC

Considering the above described assumptions Weng et al. propose two carrier

selection schemes: mobile hashing (MH) and round robin (RR). In the MH method a

random CC is selected for each MS. In the RR method a new MS (activated or handed-

over from other cell) is assigned always to the CC with the lowest number of already

assigned MSs. According to the definitions, the carrier selection is done with respect to

the CC assessment metrics defined, respectively, as:

(5.3)

(5.4)

where is size of the -th CC in terms of radio resources (e.g. bandwidth), is the

set of MSs already allocated to the -th CC, and is the set of system CCs ( ).

CC #1

CC #1

CC #2

CC #2

time

CC #1SINR

CC #2


The MH method is characterized with the binomial distribution of MSs’

allocation to CCs. For this method the probability of having out of MSs on the

-th CC is:

(5.5)

The RR CC selection method, on the other hand, always tries to equalize the

number of MSs per CC. As a result the number of MSs per CC can take at maximum

three different values:

(5.6)

where is the modulo function.

According to the above probability density functions (PDFs), the MH and RR

carrier selection schemes result in the same expected number of MSs per CC. With

respect to assumptions of Wang et al. this should translate into the same expected traffic

load per carrier. However, the two carrier selection methods differ significantly in

variation of the number of MSs per CC (see Figure 5-4). The RR method provides the

lowest possible variation of number of MSs per CCs, thus, it should also provide the

lowest variation in load per CC, and the lowest congestion probability per CC.


Figure 5-4 Probability density function of number of MSs per CC

for the MH and the RR carrier selection methods

Application of the carrier selection methods to RENs is investigated by the

author of this dissertation in [56]. The main differences between RNs and MSs that

impact the carrier selection are:

Stationary RNs observe static radio conditions, thus it is possible to make a long

term optimal CC selection.

RNs may differ in terms of coverage and MS attraction, thus load contribution of

each RN may be different.

With respect to the above two factors the following modifications to the carrier selection

methods are proposed to support RN configuration:

Channel quality can be considered in the carrier selection for RN BH links.

Load contribution of an RN should be estimated according to the actual MS

attraction rate of this RN, i.e. probability of MSs connecting to this RN.

The above modifications lead to the redefinition of the round robin carrier selection

method and the definition of two new methods for RENs: minimum interference (MI)

and minimum load (ML). The MI method is channel aware, but not load aware. It

defines carrier selection that maximizes the channel quality for all nodes, but does not

consider the allocation of other MSs and RNs. The ML method is both channel and load

aware. It considers both the channel quality and the expected resource availability per

carrier. Summary of assessment metrics for the four carrier selection methods is given in

Table 5-1.


Table 5-1 Summary of carrier assessment metrics for RN carrier selection [29, 56]

Load unaware methods Load aware methods

Channel

unaware

methods

Channel

aware

methods

where is a set of RNs allocated to the CC

Behaviour of the carrier selection methods defined above is evaluated with

respect to behaviour of an LTE-A REN (see Appendix A for modelling assumptions,

including discussion of reliability of the collected results given in Appendix A.5). Three

scenarios are considered:

A. uniform REN deployment (see Figure A-2a),

B. uniform REN deployment with additional unmanaged femto-cell interferers

deployed in random locations (5 per BS sector), and

C. REN with BSs engaged in soft frequency reuse (SFR) inter-cell interference

coordination (ICIC) scheme (see Figure 2-8).

In all scenarios 10 RNs are deployed per BS sector and operated in a two-hop topology.

In scenario A spectrum division into 4 CCs is considered. The CCs are identical

in terms of size (number of resources) and BS configuration (especially the BS

transmission power). For this reason there are no specific grounds for an RN to prefer

one carrier over another. Therefore, the MH and RR methods result in uniform

allocations of RN backhaul (BH) and access (AC) links to CCs (see Figure 5-5). The

minimum interference (MI) method avoids allocation of RN BH links to CCs occupied

by AC links of neighbour RNs. This leads to basic inter-RN interference coordination

that results in a small improvement of the BH link SINRs (0.9 dB gain in average BH

link SINRs over the channel unaware methods MH and RR, see Figure 5-6a). The

minimum load (ML) method includes channel quality and load awareness in the carrier

selection process, thus, it also provides a small improvement of the RN BH link SINRs

(0.4 dB gain in average BH link SINRs over the MH and RR methods). It also results in

practically uniform allocation of RN links to CCs, which minimizes probability of a CC

congestion.


Figure 5-5 Simulated CC allocation to (a) RN BH and (b) RN AC links in an uniform REN scenario

In terms of the BH link throughputs the MH method results in the lowest

performance. This is because the MH method does not provide coordination in any

domain. The MI method provides improved performance for all RNs (network capacity

higher by 10% than with the MH method). The gain originates from improved BH link

SINRs. The ML method also results in the improved performance (5% higher overall

network capacity than with the MH method) with the gains visible especially at low

percentiles of the cumulative distribution function (CDF). This is the outcome of the

load balancing component of this method that improves performance fairness. Finally,

also the RR method provides RN BH throughputs higher than with the MH method. The

gains come from the load balancing approach. The RR method performance is a lower

bound of the performance available with the MI and ML methods.


Figure 5-6 Simulated RN BH link statistics with various carrier selection methods in a uniform REN

scenario

The test scenarios B and C introduce a factor of diversity between system

carriers. Specifically, in scenario B three out of four CCs are occupied by interfering

femto-cells (one CC is free of femto-cell interference). The femto-cells are unmanaged

by the network operator, i.e. position and occupied CCs of the femto-cells are unknown

and uncontrolled. In scenario C, on the other hand, BSs are engaged in SFR ICIC

transmission with non-uniform power per CC as depicted in Figure 2-8, i.e. one of the

BS CCs is operated with transmission power increased by 6 dB.

Because of the diversity in radio conditions between the system CCs, it is

beneficial for RNs to occupy some CCs rather than others. The MH and RR methods,

unaware of the radio conditions in the network, result in the same RN allocation pattern

as in the uniform scenario A (see Figure 5-7). The MI method, on the other hand, aligns


to the non-uniformity of radio conditions and results in high disproportion of the RN BH

link allocation to CCs. The ML method, on the other hand, takes into account the

variation in radio conditions, but also balances load across carriers. As a result, a small

disproportion in allocation to carriers is reached in favour of the carrier providing better

radio conditions.

Figure 5-7 Simulated CC allocation to RN BH (sub-plots a,c) and RN AC (sub-plots b,d)

in test scenarios B (sub-plots a,b), and C (sub-plots c,d)

In the test scenario B the ML method allocates more RN AC links on the CC not

used by the interfering femto-cells, thus increasing the MS-observed signal quality. With

the MI method this is not possible, as most of the RNs use the femto-free carrier for the

BH link operation and cannot use it for the AC link operation (out-band resource

partitioning is assumed).


The channel aware carrier selection allows the MI and ML methods to provide

RN BH link quality improvement over the MH and RR methods (2.87 dB and 3.87 dB in

average BH link SINRs with the MI method, and 2.26 dB and 1.54 dB in average BH

link SINRs with the ML method, respectively, in scenarios B and C). With the ML

method the gains in the BH channel quality also translate to the increase in the overall

BH link throughputs (12% gain in both scenarios). The MI method, however, due to

overloading of one of the carriers with RN BH links, does not take full advantage of the

improved BH SINRs. In the test scenario C the high disproportion in CC selection to RN

BH links leads to achieving the lowest BH throughputs, even though the BH SINRs are

the highest.

Figure 5-8 Simulated RN BH link statistics with various carrier selection methods in evaluation

scenarios with unmanaged interferes (sub-plots a,b), and BS SFR ICIC (sub-plots c,d)


The presented evaluations show that the ML method typically provides the

highest performance for RNs by engaging proactive load balancing and basic

interference coordination mechanisms. This method, however, requires detailed

information on RN BH and AC link radio conditions, which may not always be available

during RN start-up. Especially, the RN AC link radio conditions should be estimated

based on MSs’ measurements in the RN cell done over time in multiple locations. If

such measurements are not available, the channel un-aware RR method can be used in

the initial stage of RN operation. The RR method provides the basic level of proactive

load balancing between RNs and improved performance over the full random MH

allocation. If detailed information on RN status (measurements of radio conditions

and/or load information) become available at later stage of the RN operation, adaptive

load and interference coordination can be introduced as proposed later in this chapter.

The Impact of Carrier Aggregation

To meet the IMT-A requirements regarding transmission bandwidth support both the

LTE-A and WiMAX systems introduce the carrier aggregation (CA) feature (in WiMAX

called channel aggregation [106]). CA allows for a device to communicate with an

access point on multiple CCs at a time. Motivation for this feature is provisioning of

superior data rates per device without the need for improving channel capacity.

The CA feature, as defined by the LTE-A standard [21], is explicitly supported

for MSs. In this work, however, it is additionally considered that the CA feature can be

applied in the same way to the RN BH links. This assumption is justified considering

that according to the LTE-A definition a DF RN is in fact a combination of the MS’s and

the BS’s functionalities [3].

A question that is especially relevant when considering CA is – what benefits

does the aggregation of carriers provide over distribution and separation of the

communicating nodes on the system carriers, i.e. the frequency-domain multiplexing

(FDM) based assignment. The two resource allocation schemes are depicted in Figure

5-9 and analysed hereafter.

Figure 5-9 Resource allocation based on (a) FDM and (b) CA


Let us consider a multi-carrier system with carriers . In this system there

are active MSs (or RNs, the specific nature of the devices is irrelevant for now), each

requesting a certain data rate . This requested data rate corresponds to a certain

resource demand for each of those MSs, i.e. a certain single MS traffic load contribution

defined as:

(5.7)

where is the average channel capacity of the MS .

Considering all the MSs active in the system, the total traffic load at the BS is:

(5.8)

In case of a multi-carrier system each of the active MSs is allocated to a single

(FDM scheme) or multiple CCs (CA scheme). For the time being let us assume that the

division of the MS’s load between carriers is uniform, i.e. it is true that:

(5.9)

where is the set of CCs used by the MS .

With respect to a certain allocation of MSs to CCs the traffic load per CC is:

(5.10)

Then the expected value of the traffic load per CC is:

(5.11)

and its variance is:

(5.12)

Now let us make two assumptions about the resource allocation: (1) allocation of

MSs to CCs is fully random, and (2) all MSs aggregate the same number of CCs. The


first assumption corresponds to a scenario, in which all CCs are statistically equivalent

and no load balancing algorithms are applied. This assumption is fulfilled easily,

especially when most basic implementations of multi-carrier systems are considered.

The second assumption is less realistic, especially when considering that in a system

MSs of various classes may coexist (e.g. supporting CA and not supporting it).

However, making those assumptions allows illustrating the impact of CA on system

performance without significant loss of generality. When the two assumptions are true,

the above statistics of the traffic load per CC can be reformulated as:

(5.13)

The above equations allow making the following conclusions for a uniform

resource allocation as assumed earlier:

The expected traffic load per CC is the same as the expected traffic load for the

whole multi-carrier system and does not depend on the allocation of MSs to CCs.

Variance of the traffic load per CC is inversely proportional to the number of

CCs aggregated per MS.

The two observations are depicted in Figure 5-10 for an exemplary test scenario

with five CCs, 70% average system load and 65% basic load variation across carriers

(evaluation presented in [59]). Without CA enabled this scenario is characterised with

19% probability of carrier overload. If CA is enabled the overload probability decreases

with number of aggregated carriers per device. With aggregation of three carriers the

overload probability decreases to 11%, and with aggregation of five carriers it is only

2%.


Figure 5-10 Carrier load characteristics with FDM and several levels of CA [59]

Taking into account equations (5.13) and the above figure it can be concluded

that the main benefits of CA-based resource allocation over FDM-base allocation are:

decrease of probability of overloading a single CC of a multi-carrier system,

support for higher peak data rates per receiving device.

Therefore the CA-based resource allocation does not change the resource availability per

device, but rather provides better support for traffic variability between devices. This is

especially important when considering relay-enhanced networks (RENs), as in such

networks MSs coexist with RNs, and each RN may represent traffic load generated by a

various number of MSs.

As simulation data collected by the author show [55], resource requirements of a

single RN can exceed capacity of a single CC in a multi-carrier scenario. This takes

place, e.g., when multiple MSs are found in the RN coverage area, or when the RN-

connected MSs have high traffic demands. Without CA enabled for RNs, such an RN

with high resource demand observes performance limitation even if the donor BS has

under-loaded carriers available (see Figure 5-11 for , where is number of

aggregated BH CCs). If CA is enabled for RNs, they can take advantage of multiple

backhaul link CCs at the same time, thus achieving higher peak capacity (see Figure

5-11 for ).


Figure 5-11 Simulated resource availability vs. resource demand in a two-hop REN [55]

Aggregation of multiple carriers per each RN also leads to more optimal resource

allocation. The more optimal allocation means that the resources are allocated to the

demands of nodes. In Figure 5-11 this is depicted as narrowing of the resource

availability vs. demand plots with increasing number of aggregated carriers. The multi-

carrier diversity enables balancing of resource over-provisioning on under-loaded

carriers with resource under-provisioning observed on overloaded carriers.

Even stronger need for CA is observed with multi-hop relaying. The first-hop

RNs in such a topology need to support not only traffic demands of own MSs, but also

of MSs connected to their subordinate RNs. This situation is depicted in Figure 5-12.

The simulation data indicate that with multi-hop relaying even aggregation of two out of

five carriers may not be sufficient in some cases.


Figure 5-12 Simulated resource availability vs. resource demand

of first-hop RNs in a multi-hop REN [55]

5.2.3 Load-Aware Adaptation

In Section 3.4 RRM principles for an efficient relay-enhanced network (REN) operation

are defined. The principles describe, inter alia, optimal RN resource partitioning.

According to formula (3.59) the amount of radio resources that should be dedicated for

operation of RN backhaul (BH) and access (AC) links respectively is a function of the

relaying gain. The relaying gain is a ratio of the expected average AC link capacity to

the expected BH link capacity. The BH link capacity can be measured directly by the

RN and, if needed, reported to an external control unit (e.g. centralized operation and

maintenance entity, OAM). The average AC link capacity, however, depends on the

distribution of MSs in the RN cell and channel quality measured by those MSs. As such,

the average AC link capacity is unknown during the RN start-up. Hence, it is not

possible to estimate a priori relaying gain of an RN and provide for this RN the optimal

resource partitioning configuration from the moment of its first activation. Therefore, a

practical approach to the resource partitioning is to:

1) apply during RN start-up a default resource partitioning configuration (e.g. the

same for all RNs or dependent on RN BH link quality only),

2) collect MSs’ measurements of the RN AC link signal quality during RN

operation, and

3) adapt resource partitioning configuration according to the estimated relaying gain

of the RN (periodically or event triggered).

This section describes proposals of the resource partitioning adaptation for RNs

operated in multi-carrier systems. The procedures are characterized as short- and long-


term adaptation. The short term adaptation procedure controls AC component carrier

(CC) activity and provides reduction of RN power consumption and RN-originating

interference. This adaptation is done on the basis of load on the RN AC CCs. The long-

term adaptation procedure modifies CC assignment to the BH and AC links of an RN to

optimize the carrier-based resource partitioning. The trigger for this optimization is load

imbalance between the RN BH and AC CCs. The two procedures were proposed by the

author of this thesis for the first time in [26] and further evaluated in [30].

Short-Term SCC Adaptation

With multi-carrier resource partitioning at least one component carrier is assigned to the

backhaul (BH) link of an RN and at least one component carrier is assigned to the access

(AC) link of the RN. Out of those carriers one backhaul carrier and one access carrier

are called the primary component carriers (PCCs).The PCC is the anchor CC for devices

connecting to an access point. It provides the most significant control information for

every radio link, e.g. RRC signalling. For out-band RNs the backhaul and access PCCs

are different component carriers. For hybrid RNs the backhaul and access PCCs are the

same component carrier, i.e. the in-band carrier. For both the out-band and the hybrid

RNs the additional aggregated BH and AC CCs are called the secondary CCs (SCCs).

The SCCs are used to provide additional resources for user plane data transmission.

To support legacy MSs an RN should transmit control channels (at least

broadcast and pilot) on all of its AC CCs. The control channels on AC CCs consume RN

power and are source of interference to neighbouring cells even if there are no data

transmissions taking place on the RN AC link. With the short-term adaptation procedure

it is proposed that: if an RN has multiple AC CCs, i.e. at least one AC SCC, and load at

the RN cell is low, some or all of the AC SCCs can be temporarily deactivated.

The deactivation of the AC SCCs should not limit transmissions on the RN AC

link, i.e. should not lead to congestion on the active AC CCs. Therefore, an AC SCC

should be deactivated only if the RN AC load decreases below a certain safety threshold

level, and it should be reactivated as soon as the RN AC load increases above a different

threshold level. To avoid “Ping-Pong” switching an activity hysteresis should be used

with the deactivation threshold level below the activation threshold level. Therefore, the

AC SCC deactivation condition can be defined as:

(5.14)

and the AC SCC activation condition as:

(5.15)

where is the resource utilization on the AC link of the RN cumulated over all AC

CCs, and are, respectively, the deactivation and activation thresholds (


, e.g. and ), and denotes the set of active AC

SCCs of the RN . Furthermore, a triggering time can be used to avoid triggering

(de)activation on temporary load fluctuations (in LTE-A the triggering time can be, e.g.,

the 40 ms broadcast update period or its multiplicity). Operation of the short-term

adaptation procedure following the above stated conditions is depicted in Figure 5-13.

Figure 5-13 AC SCC activation/deactivation function [26]

The algorithm for the proposed short-term RN load adaptation procedure is

depicted in Table 5-2. The procedure can be performed by each RN in a fully

autonomous manner. It might be, however, required from the RN to inform its

neighbours about any change in the CC configuration, e.g. as specified in the LTE-A

standard [10].

Toff Ton

δoff

δon

Deactivation of

an AC SCC

Activation of an

AC SCC

Time

AC

lin

k r

eso

urc

es

Resource utilization

(load) on RN AC CCs

PCC+SCC

PCC

PCC+SCC


Table 5-2 Short-term RN load-aware adaptation procedure [26]

FOR every TTI:

RECORD: RN AC link resource utilization

IF:

for last TTIs

% deactivate AC SCC with the lowest assessment metric:

DO:

ELSEIF:

for last TTIs

% activate AC SCC with the highest assessment metric:

DO:

ENDIF

ENDFOR

where and are, respectively, deactivation and activation trigger timers

Operation of the above described RN AC SCC adaptation algorithm is verified

next via LTE-A system simulations (see Appendix A for modelling details, including

discussion of reliability of the collected results given in Appendix A.5). In the

simulations a number of MSs using various traffic types is allowed to roam freely in an

REN. The system utilizes 5 CCS and RNs are in the out-band configuration. Depending

on the estimated relaying gain each RN is configured with 1-4 AC CCs (see Figure

5-14). Without the dynamic AC SCC adaptation all the AC CCs are constantly active

transmitting at least control channels (pilot and broadcast). If the dynamic AC SCC

adaptation procedure is implemented in this network, the average number of active RN

AC CCs can be reduced from 2.61 to 1.37, i.e. almost twice. This way the REN network

can achieve on average power savings of 12.4% of a CC transmission power per RN.

With default 3GPP defined RN configuration (1 W transmission power at 10 MHz

carrier), this means savings of 0.124 W per RN (26 W over 210 RNs in the test scenario)

just in the transmitted power. In addition further power savings could be possible by

deactivation of elements of RN transceivers (e.g. power amplifiers) that support

operation of the deactivated SCCs.


Figure 5-14 Simulated RN AC activity with dynamic SCC adaptation

A negative effect of the AC SCC deactivation is a probability of RN AC

congestion when not all of the AC CCs are active. This is depicted in Figure 5-14. The

congestion takes place if load on an RN AC link increases fast and reaches full resource

utilization on the active AC CCs before additional AC SCC is activated. This effect can

be minimized by appropriate tuning of the activation threshold and activation trigger

time . With optimized settings of those two parameters the congestion should not

have a critical impact on the end-user performance, as it will just increase slightly the

overall transmission time per packet and not decrease the average data rate. The

collected simulation results indicate that in the tested scenario, with the SCC adaptation

parameters set to: , , , the average data rate

per MSs is actually increased by 1.5-2% compared to the case without the dynamic AC

SCC adaptation. The improvement of average data rates results from reduced amount of

interference generated by the RN AC CCs. As depicted in Figure 5-15, without the

underutilized AC CCs being active and transmitting control channels both the RN BH

link SINRs and MS observed SINRs are increased. The average gains observed by RNs

on the BH links are at the level of 1.1 dB and the gains observed by the MSs are at the

level of 0.3 dB. The gains are not significant. The results prove, however, that with the

dynamic AC SCC adaptation performance of a network can be at least sustained, while

benefiting from reduced power consumption.


Figure 5-15 Simulated SINR improvement due to RN AC SCC adaptation

Long-Term SCC Adaptation

Purpose of the long-term adaptation procedure is to optimize resource partitioning

configuration for RNs operated on multiple carriers. In this procedure long-term

statistics of the RN AC link load are collected and used as a trigger for carrier based

BH/AC balancing. Specifically, if the long term AC load statistics indicate that the RN

AC link is typically overloaded, the RN is most probably AC limited and more AC link

resources are required. On the other hand, if the long term AC load statistics indicate

that the RN AC link resources are constantly underutilized, e.g. one AC CC is never

used, the RN is most probably BH limited and more BH link resources are required. In

both cases reconfiguration of a multi-carrier operated RN has the form of

reconfiguration of an AC SCC to a BH SCC or vice versa.

The proposed algorithm for the long term adaptation procedure is described in

Table 5-3. According to this proposal an RRM control entity (RN, BS or OAM

depending on the system configuration) records information on activity of AC SCCs of

an RN. The collection of RN AC SCC status information is done with sampling period

(the sampling period can be one transmission time interval (TTI)). Next, in

every predefined reconfiguration period an adaptation step is initialized.

During the adaptation step the probability of having all configured AC SCCs active is

calculated. This probability is estimated as the ratio of the cumulated time of activation

of all AC SCCs during the last inter-reconfiguration time to the inter-

reconfiguration time. If the estimated probability is lower than the AC-to-BH

reconfiguration threshold level ( ) and the RN has at least one AC

SCC configured, the AC SCC with the highest assessment metric (see Table 5-1) should

be reconfigured to be a new BH SCC. Otherwise, if the estimated probability is higher


than the BH-to-AC reconfiguration threshold level ( ) and the RN has

at least one BH SCC configured, the BH SCC with the lowest assessment metric should

be reconfigured to be a new AC SCC. If radio and traffic conditions in the network are

stable, each RN should reach a stable configuration after several reconfiguration

iterations. Further reconfigurations can be made if changes in the network state occur.

Table 5-3 Long-term RN load-aware adaptation procedure [26]

FOR every :

RECORD: AC link SCC activity

IF:

IF: and

% reconfigure AC SCC with the highest assessment metric to be BH SCC:

DO:

DO:

ELSEIF: and

% reconfigure BH SCC with the lowest assessment metric to be AC SCC:

DO:

DO:

ENDIF

ENDIF

ENDFOR

where and are, respectively, AC SCC activity sampling period and long-term

reconfiguration period, and are, respectively, AC-to-BH and BH-to-AC SCC reconfiguration

thresholds, is the set of BH SCCs of the RN , and

is the carrier assessment metric following

one of the methods defined in Table 5-1.

Operation of the above described long term adaptation algorithm is verified next

via LTE-A system simulations (see Appendix A for modelling details, including

discussion of reliability of the collected results given in Appendix A.5). In the

simulations a multi-hop REN is assumed with system operated on 5 CCs with the RNs

using the out-band configuration. Starting configuration for the 1st hop RNs is a

configuration with 3 BH CCs and 2 AC CCs. Configurations of the further hop RNs

depend on the AC CC availability at superior RN, but always CC division close to 50:50

proportion is targeted. Next, configurations of RNs’ S s are adapted according to the

procedure described in Table 5-3. The resultant CC allocation for RN BH links at

consecutive relaying hops is depicted in Figure 5-16. The 1st hop RNs, i.e. the RNs


connected directly to BSs typically use 3 or 4 CCs for BH operation. Those RNs are

serving also subordinate RNs, thus require high amount of BH link capacity. The 2nd

and

3rd

hop RNs, on the other hand, have lower capacity demands, thus they typically use 2

or 3 BH CCs. The 2nd

hop RNs typically use only 2 BH CCs because they are limited by

the availability of AC CCs at the 1st hop RNs.

Figure 5-16 Simulated CC allocation to RN BH links with long-term load-aware adaptation [30]

As depicted in Figure 5-17 the long-term SCC adaptation procedure leads also to

increase of the average data rates for all MSs. Specifically, the throughput increase

observed by the RN-connected MSs is at the level of 6% and the increase observed by

the BS-connected MSs is at the level of 1.5%. On average the gains calculated over all

MSs are expected to be in range of 3.5-4% [30]. Source of those gains is improved

capacity balancing for relayed links and, thus, elimination of bottlenecks.

Figure 5-17 Simulated performance of multi-hop REN with long-term load-aware adaptation [30]


5.3 Inter-Cell Interference Coordination

5.3.1 Principles

Capacity of a radio link in a cellular system is either coverage or interference limited.

Coverage limitation occurs when the received signal power drops below the thermal

noise power. This may happen especially in big cells and cells without any direct cell-

neighbours. In modern cellular networks, however, more common are deployments with

high density of access points. With such deployments strong interference coupling

between neighbouring cells may take place. This leads to interference-based capacity

limitation of radio links.

In traditional homogeneous networks the inter-cell interference coupling can be

to some extent managed by applying frequency reuse schemes described earlier in

Section 2.3. The baseline inter-cell interference coordination (ICIC) is, however,

insufficient in case of heterogeneous networks. When low power access points are co-

deployed with macro base stations not only the macro-macro interference needs to be

considered, but also the macro-low power node and low power node-low power node

interference have to be taken into consideration. In heterogeneous deployments the ICIC

problem is also more complex than in homogeneous deployments, as typically more

nodes are involved in the coordination.

Baseline ICIC concepts for homogeneous and heterogeneous relay-less networks

(RLNs) consider only coordination of resource utilization by access links of macro and

low power access points. Example of such solution is the autonomous component carrier

selection (ACCS) concept proposed by Garcia et al. for multi-carrier femto-enhanced

networks [49, 50]. In this concept each femto node measures incoming interference at its

location and selects for its operation the carrier characterised with the lowest

interference level. In an extended version of this concept also inter-femto node

negotiations may take place to improve the coordination.

The baseline ICIC approach is valid for relay-less networks as the fixed backhaul

(BH) link of non-relaying nodes is not affected by radio interference. In case of relay-

enhanced networks (RENs), however, the wireless BH links of RNs can be affected by

radio interference on a similar level as the access (AC) links to MSs. For this reason the

ICIC procedure for RENs needs to be a twofold process focused on one side on

optimization of the MS observed AC link quality and on the other side on maintaining

sufficient capacity of RN BH links.

Figure 5-18 depicts the downlink interference coupling mechanisms affecting

RN operation. In a two-hop REN the mechanisms are [54, 59]:

Access-to-Backhaul (A2B) interference, i.e., interference observed on the BH

link of an RN, generated by AC link of a neighbour RN,


Access-to-Access (A2A) interference, i.e., interference observed on the AC link

of an RN, generated by the AC link of a neighbour RN,

Direct-to-access (D2A) interference, i.e., interference observed on the AC link of

an RN, generated by the BS.

In a multi-hop REN additionally direct-to-backhaul (D2B) interference may occur. The

D2B interference takes place when a BS interferes with an inter-RN BH communication.

The D2B interference is, however, not considered in this work. There are two reasons

for that. Firstly, it is assumed that the multi-hop inter-RN BH communication takes

place only in case of poor BH signal quality towards the BS. In such a case the expected

impact of the D2B interference on the RN operation is negligible. Furthermore, it is

recommended in this dissertation that BSs should utilize all system resources and, thus,

do not fall under the proposed ICIC scheme.

Figure 5-18 Interference coupling mechanisms in RENs [54]

The principle for an REN ICIC is to coordinate resource allocation to RN AC

and BH links so to minimize the overall interference-coupling impact on the two link

types. Specifically, the same groups of resources should not be assigned to the AC link

of an RN and to the BH link of another RN if they are characterised with high A2B

interference coupling potential. The REN-specific problem is, however, that the

coordination steps leading to elimination of the A2A interference may lead to generation

of the A2B interference, and vice versa (see Figure 5-19). This problem is especially

unavoidable if BH and AC links of every RN occupy jointly all system CCs.

A2A interference

A2B interference

D2A interferenceBS

RN2

RN1

signal

interference


Figure 5-19 REN ICIC dilemma [59]

The following sections present a proposal of a carrier-based ICIC scheme for

RENs. The proposed concept aims at elimination of the A2A and A2B inter-RN

interference in a way that balances the impact of the two types of interference on the

end-to-end RN performance. The proposed ICIC scheme was described by the author of

this dissertation for the first time in [54], and in a modified version in [59]. Evaluations

of the centralized version of the ICIC scheme applied to two- and multi-hop REN

scenarios are also described in [26] and in [30], respectively. In this dissertation findings

of the earlier works are collected and summarized. Also performance evaluations of

decentralized versions of the ICIC scheme are included. The evaluations are also

conducted for heterogeneous networks with variable deployment. Purpose of those tests

is to assess adaptation capabilities of the proposed solution with respect to dynamic

radio conditions.

5.3.2 Carrier-Based ICIC Concept Proposal

The proposed hereafter carrier-based ICIC concept assumes coordinated optimization of

carrier allocation to RN BH and AC links. Starting from a default carrier assignment

RN2 AC reallocated

from CC #2 to CC #1

CC #1

CC #2

signal

interference

Access-to-access

interference on CC #2

BS RN2RN1

Access-to-backhaul

interference on CC #1

BS RN2RN1


applied during RN start-up, e.g. resulting from the round robin selection method (see

Section 5.2.2), the carrier assignment can be altered gradually as RN AC and BH link

measurements are collected and/or radio conditions in the network change.

The proposed ICIC concept is a two-step procedure. Firstly, measurements of the

RN AC and BH links are processed to detect inter-RN interference coupling problems.

Secondly, appropriate reconfiguration decisions are made to eliminated the interference

coupling or minimize its impact on the overall performance [54, 59]. Depending on the

assumed coordination scheme, those steps are done either at RNs, at the donor BS, or at

the OAM entity in the core network.

Detection of Inter-RN Interference Coupling

Detection of inter-RN interference coupling involves detection of both the A2A and

A2B types of interference. To handle the REN ICIC dilemma depicted in Figure 5-19 it

is proposed that for detection of the two types of interference separate sensitivity levels

should be used. The sensitivity levels are denoted as and respectively for the

A2B and A2A interference. The sensitivity levels correspond to the preferred target

SINR levels for the RN BH and AC links, respectively, and are defined as the maximum

allowed interference-to-signal (ISR) ratios. Only the interference coupling events that

are stronger than the defined sensitivity levels are considered for coordination.

In the process of evaluation of the RN BH and AC link measurements the ISR is

calculated for every detected interferer. The ISR for the A2B and A2A interference

relative to the assumed sensitivity levels and is calculated, respectively, as:

(5.16)

(5.17)

where is the A2B ISR observed by the victim RN from the aggressor node ,

is the A2A ISR observed by the MS connected to the victim RN from the

aggressor node , is the BH link signal power received by the RN from its donor

node, is the signal power received by the RN from the node , and by analogy

is the signal power received by the MS from the node . All measurements and

calculations are done per carrier (indexed with ) and given in the linear scale. The

aggressor node can be any type of access point (e.g. relay, pico or femto nodes),

however, macro BSs can be excluded as superior nodes not subject to the coordination.

Detection of the A2B interference can be done by an RN on the basis of its own

measurements. Detection of the A2A interference, on the other hand, should be done on

the basis of measurements performed and reported by the RN-connected MSs. Such


approach enables more accurate assessment of the MS-perceived performance than the

estimation based only on RN own measurements. Therefore, an RN should:

(1) collect measurements of its served MSs,

(2) calculate the A2A ISR values for every measurement report, and

(3) average the A2A ISR per aggressor node.

This way an ISR value relevant for the whole RN cell is estimated. Especially, the RN

cell-edge status is included in the assessment. The A2A ISR averaging function can be,

e.g., a harmonic average, to focus on cell-edge performance, or an arithmetic average, to

reflect the overall RN AC link performance. In this work the arithmetic averaging is

used, therefore the resultant A2A ISR is:

(5.18)

where denotes the set of all positions in which A2A ISR measurements were taken.

Those can be measurements of one MS done in various locations, or measurements of

multiple MSs connected to the RN .

Detection of the A2A and A2B interference is done by each RN individually.

Later, depending on the implementation of the ICIC scheme, each RN can forward

information on the detected interference to a central coordination entity, or exchange the

information with other RNs in its neighbourhood. On the basis of the collected

information on the interference coupling cases the reconfiguration steps can be made.

Inter-RN Interference Coupling Resolution

The proposed process of providing coordination to multi-carrier RENs is based on an

iterative resolution of the detected inter-RN high interference coupling cases. In each

iteration of the coordination procedure the following steps are made:

(1) decide which of the conflicting nodes should be reconfigured, and

(2) select a configuration that minimizes the cumulated interference strength of

interference experienced and/or generated by the reconfigured RN.

The selection of the RNs for reconfiguration is done on the basis of the

cumulated interference strength (CIS). The general CIS formula is:

(5.19)


where and

are, respectively, sets of BH and AC CCs of the RN .

For every RN engaged in a high interference coupling case the existing

cumulated interference strength (CIS) is estimated, as well as the minimal CIS available

after reconfiguration of this RN. That RN should be selected for reconfiguration which

provides the highest CIS reduction. To avoid reconfiguration of victim and aggressor

nodes at the same time it is proposed that only one RN should be reconfigured per BS

sector in an iteration of the coordination process.

The carrier assignment configuration that minimizes the overall strength of

interference related to the RN can be found on the basis of the following carrier

assessment metric:

(5.20)

BH CCs are allocated according to decreasing values of the metric, while the

AC CCs are allocated according to increasing values of the metric.

Depending on the considered level of cooperation the above formulas (5.19) and

(5.20) may include:

only information on the incoming interference (selfish coordination),

information on the incoming interference and information on the interference

outgoing towards a certain group of RNs, e.g. served by the same donor BS

(cooperative coordination with fractional system information), or

information on the incoming interference and on all the outgoing interference

(cooperative coordination with full system information).

5.3.3 Evaluation of the Carrier-Based ICIC Concept

Operation of the proposed carrier-based ICIC concept has been verified on the basis of

LTE-A system simulations. In the simulations a densely deployed two-hop REN

network with 10 RNs per BS sector was assumed (dense-urban scenario, see Appendix

A for modelling details). System spectrum was organized in three CCs of equal size.

To model dynamics of radio conditions, in the middle of the simulation a number

of femto-cells was activated in the test network (5 femto-cells per BS sector). Such

change may, e.g., reflect a situation when people activate their private cells after coming

back from work. The femto-cells are uncontrolled by the system operator and their


locations and carrier allocation are unknown. Offloading effect of the femto-cells is not

considered as it is assumed that the femto-cells are configured with the closed subscriber

group (CSG) [57, 68] access control. The result of the change in radio conditions is a

significant increase of interference. It is expected that the proposed ICIC procedure

should adapt configuration of RNs to the new situation and improve the system

performance.

Centralized Coordination Scheme

Let us start with a centralized coordination scheme, i.e. cooperative coordination with

full system information. This coordination scheme explicitly studied by the author of

this dissertation in [54] and in [59]. Due to availability of full system status information

this scheme is characterised with the highest coordination potential. Provisioning of the

full system status information to the decisive entity requires, however, significant

signalling efforts. This is especially true in case of multi-hop topologies (see Figure

5-20).

Figure 5-20 System status information collection for a centralized management

According to the collected simulation data, various A2A and A2B sensitivity

levels used for the interference coupling detection result in various available system

capacity gains (see Figure 5-21). The highest gains (~10%) are observed in simulations,

in which the A2B sensitivity is 16 dB higher than the A2A sensitivity. If this relation is

not provided, either BH ( ) or AC ( ) link

optimization is oversensitive. In such a case, aggressive elimination of A2B interference

leads to generation of strong A2A interference and the system becomes limited on AC

links. If A2A interference elimination is oversensitive, the system becomes limited on

BH links.

OAM

Control signalling


Figure 5-21 Centralized coordination gains as a function of interference detection sensitivities [59]

Timeline of centralized ICIC reconfigurations performed in a test network with

sensitivity levels set to and is depicted in Figure 5-22.

Starting from a default carrier assignment (round robin method, see Section 5.2.2)

average system capacity is gradually increased as interference coordination is provided.

The saturation state with gain at the level of 10% is achieved after 5-6 ICIC iterations.

Figure 5-22 Simulated timeline of centralized ICIC process

In the simulation experiment, after iteration 25, the additional femto-cells are

activated and interference conditions in the network change. The result of the change of

radio conditions is a significant decrease of average system capacity (offloading effect


of the femto-cells is not considered). The proposed ICIC procedure automatically detects

the new interference and adapts RN configuration accordingly. After three additional

reconfigurations the average system capacity is increased to the level 12% higher than

that observed in the network not using ICIC coordination.

Decentralized Coordination Schemes

A fully centralized coordination scheme requires collection at a central management

entity of status information from all nodes in the network. This consumes time, as well

as radio resources. To minimize impact of the control information signaling some of the

decisive competences may be transferred to RNs. In such case each RN should be able

to control its own AC link configuration, while its BH link configuration is managed by

its direct donor node. Therefore, the decentralized management schemes should be done

in two cycles:

AC link adaptation – related to configuration of a single RN, thus can be

executed on a relatively short time basis,

BH link adaptation – related to operation of multiple RNs and requiring status

information from a wider area, thus, performed on a longer time basis.

Next, two coordination schemes following this model are described: distributed

(cooperative) and autonomous (selfish).

In the distributed management scheme each RN communicates with its closest

neighbors, e.g. RNs served from the same donor BS, to exchange system status

information. As the control plane communication takes place with a limited number of

nodes and on a short range, limited signaling overhead is expected (see Figure 5-23). At

the same time each RN has status information of its direct neighborhood, i.e., the nodes

with which the strongest interference coupling may take place.

Figure 5-23 System status information exchange for a distributed management

Control signalling


In the autonomous management scheme RNs do not exchange status information

with each other. This way each RN performs reconfigurations based just on its own

measurements and is unaware how its operation impacts other RNs. Each RN behaves in

a selfish manner, however, a certain level of cooperation is provided anyway by the

donor node controlling configuration of BH links.

Figure 5-24 depicts comparison of network coordination timeline when using

centralized, distributed and autonomous management schemes. For the distributed and

autonomous schemes it is assumed that the AC link adaptation steps can be performed

ten times more frequently than the BH reconfigurations. The BH reconfigurations of the

distributed and autonomous schemes are performed as often as the full reconfigurations

of the centralized scheme. As the results show, the distributed and autonomous schemes

initially provide faster increase of performance. This is because the AC link

reconfigurations can be done more frequently than the centralized full reconfigurations

(less time required for collection of system status information). The centralized

management scheme, however, quickly catches up and at the end provides the highest

performance. The performance available with the distributed management scheme is

only slightly lower. And the performance available with the autonomous management

scheme is the lowest, however, still approximately 8% higher than in case of a system

without the ICIC procedure implemented (11% after activation of femto-cells). The

main advantage of the autonomous scheme, however, is the lowest complexity of the

management functionality and the lowest signaling overhead.


Figure 5-24 Comparison of centralized, distributed and autonomous ICIC schemes:

(a) at system start-up, and (b) at recovery from change in the network deployment

5.4 Summary

In this chapter two carrier based adaptation concepts have been proposed: carrier load

balancing and interference coordination. The common feature of the proposed concepts

is the ability to adapt REN configuration to changing traffic and radio conditions. This is

achieved by requesting form RNs to perform continuous assessment of their

performance and adapt iteratively if any problems are detected.

Purpose of the carrier load balancing is to steer carrier assignment to RN BH and

AC links so to avoid congestion, i.e. guarantee resource availability to the links

requiring the resources at specific time instance. In this context two complementary

approaches are proposed: proactive and adaptive. The proactive approach tries to

anticipate load generated by each communicating node (MS and/or RN) with respect to


the perceived radio conditions. On this basis the initial carrier allocation for newly

activated nodes, or nodes handed-over from neighbouring BS cells can be decided on in

a load and/or channel quality manner. Furthermore, the carrier aggregation concept can

be applied to RN BH links to provide support for higher peak data rates and lower

congestion probability per carrier.

The proactive load balancing concepts are the basic means of load-aware

optimization of carrier allocation to MS and RN nodes. Further performance increase is

available when using adaptive load balancing schemes. In this chapter it is proposed to

apply in RENs adaptation schemes based on secondary component carrier (SCC)

reconfiguration. Specifically, it is proposed to perform: (1) dynamic AC SCC activity

control, providing reduction of RN power consumption and RN-generated interference,

and (2) adaptive BH/AC capacity balancing based on SCC reconfiguration, leading to

improved bottleneck avoidance.

The second concept proposed in this chapter is the adaptive ICIC for RENs. The

main difference between the proposed ICIC scheme and existing carrier-based ICIC

concepts for relay-less networks is that in case of RENs both the MS- and RN-perceived

link qualities need to be improved at the same time to achieve overall performance

increase. As the RN BH and RN AC links may be characterized with different

interference conditions (e.g. if RNs use directional and MSs omni-directional antennas)

individual approaches are needed for each link type. The proposed concept assumes

separate interference detection levels to be used for BH and AC links. On this basis a

more restrictive coordination approach can be used for one link type, and a more tolerant

one for the other link type. The conducted analysis shows that higher performance is

achieved if the ICIC procedure is focused more on the improvement of the RN BH link

SINRs. The proposed ICIC concept can be implemented in a fully centralized or

distributed manner. Evaluation conducted in various implementation configurations

show that the procedure provides gains in every configuration, however, the achieved

gains depend on the availability of system status information.

131

Chapter 6 Summarizing the Results and Conclusions

This dissertation presents a summary of the author’s research work done in the field of

resource management for multi-carrier relay-enhanced network. Two main problems are

treated in this work, namely:

operation of advanced relay nodes (RNs) in multi-carrier systems, and

dynamic management of relay-enhanced networks (RENs).

With respect to those problems the benefits of multi-carrier REN operation are identified

and a comprehensive RRM framework for multi-carrier RENs is proposed.

In this dissertation, first of all, the relations existing in an REN between an RN,

its donor node, and its subordinate nodes are identified. In the flow of the analysis the

answers to, inter alia, the following questions are given:

What should be the criteria for resource allocation to RNs at a donor node?

Can the resource allocation to RNs be static, or should it be dynamic?

What should be the proportions between RN backhaul and access link assigned

resources?

How to avoid transmission bottlenecks over multi-hop transmission links?

The answer is also given to probably the most important problem:

How to manage resources in an REN to achieve fair performance provisioning

for all users, disregard of the type of their direct serving node?

For each of those problems analytical description is provided characterizing the resource

allocation optimal from the fairness and resource utilization efficiency points of view. In

particular, consideration of the fairness criteria is characteristic for this work, as

typically in the state of the art works RNs are treated only as means of providing

coverage and not necessary performance improvements to a cellular network.

On the basis of the conducted analysis of the REN RRM relations, a QoS-aware

resource management scheme is proposed for multi-hop RENs. The proposed scheme is

an extension of RRM concepts for relay-less networks existing in the state of the art

literature. The baseline concepts are unaware of the cross-relations existing in RENs.

Therefore the baseline concepts do not guarantee a fair performance provisioning for all

MSs, typically penalizing the RN-connected MSs. The proposed RRM scheme takes into

account the specific requirements of multi-hop transmissions, and thus, is capable of

providing a satisfactory level of performance for all MSs in a network incorporating

multi-hop relaying.

132 CHAPTER 6 SUMMARIZING THE RESULTS AND CONCLUSIONS

The proposed QoS-aware RRM scheme is based on the utility theory. The

introduced enhancements to the concept provide:

Estimation of the end-user utility function with respect to the statistics of

component links in a multi-hop connection. This includes bottleneck detection

and prediction of the end-to-end packet transmission delays.

Possibility to perform the multi-hop REN RRM in a decentralized manner by

division of the multi-level tree topology RRM problem into a set of one-level

problems. At the same the overall RRM fairness is maintained by means of

distribution of the utility information bottom-up in the multi-hop topology and

treating RNs as “super-users” with cumulated traffic needs of their

corresponding sub-networks.

Lack of functional QoS-aware RRM concepts for multi-hop RENs is currently one of the

main factors limiting practical implementation of RENs. Therefore it is the author’s

belief that concepts like the one proposed in this dissertation can significantly impact

attractiveness of the relaying concept for the future networks.

This dissertation provides also a detailed comparison of the single and multi-

carrier REN configurations. It is shown that the single-carrier RN operation is

characterised with various limitations that make its application in 4G networks sub-

optimal. Explicitly, the limitations of single-carrier relaying make this type of RN

operation practically not capable to support multi-hop topologies in an efficient manner.

The main shortcomings manifest themselves, specifically, in form of low fairness and

very high packet transmission time over multi-hop links. On the other hand, the baseline

multi-carrier RN configuration is characterised with low resolution of resource

partitioning, which may also lead to low RRM fairness. In this field the author’s

proposal is to:

(1) apply the carrier aggregation concept to RN backhaul and access links, and

(2) use a hybrid RN configuration based on both time and frequency domain

resource partitioning.

The hybrid RN configuration proposed by the author combines advantages of the

baseline single-carrier in-band and multi-carrier out-band RN configurations. At the

same it lacks most of the drawbacks of the baseline configurations.

Last but not least, dynamic carrier-based coordination concepts for RENs are

proposed in this dissertation. This includes carrier based load balancing and

interference-coordination concepts. These concepts are designed to take advantage of the

fluctuations of the traffic load and radio conditions in the network, and adapt RN

configuration accordingly. The two concepts are evaluated in realistic scenarios,

including variable service types used by users, or unpredicted changes in the network,

CHAPTER 6 SUMMARIZING THE RESULTS AND CONCLUSIONS 133

e.g. related to the presence of femto-cells not controlled by the network operator. The

proposed coordination schemes prove to provide performance improvement over static

network configurations.

Summarizing, according to the author’s opinion the main achievements of the

research conducted by the author and described in this dissertation are:

definition of key criteria for an effective and fair RRM in RENs,

proposal of a QoS-aware RRM concept for multi-hop RENs,

detailed analysis and comparison of baseline single- and multi-carrier RN

configurations,

proposal of a new RN configuration scheme (i.e. the hybrid relaying)

incorporating carrier aggregation concept,

proposal of carrier based load balancing concepts for RENs, including proactive

and adaptive carrier allocation schemes,

proposal of an adaptive carrier based interference coordination concept for

RENs.

In addition, this dissertation also provides summaries of some of the most relevant state

of the art concepts in the fields of: relaying and resource management. The conducted

work is also aligned with the technology roadmaps of the currently developed 4G

systems such as, e.g. the LTE-A system. It is, therefore, the author’s believe that the

concepts presented in this dissertation are possible to be implemented in the future

cellular networks.

With respect to the above listed contributions of the dissertation and the

results presented in its main body it can be concluded that the two theses of the

work stated in the introduction section are fully addressed and confirmed.

The work presented in this dissertation provides a set of concepts for managing

4G networks incorporating RNs. The concepts are, however, verified only with respect

to downlink transmission direction. It is up to future works to study functionality of the

proposed concepts with respect to uplink transmission direction. Furthermore, the

presented study focuses on DF relaying only. Some of the proposed concepts could be

applicable also to other relaying functionalities. Especially, conducting a similar analysis

for cooperative relaying schemes is in the author’s opinion highly relevant to future (i.e.

beyond 4G) systems. Last but not least, the presented study can be in the future extended

over mesh-type REN topologies. Again, this study direction is relevant in the context of

beyond-4G systems, for which decentralized operation schemes are foreseen.

134 CHAPTER 6 SUMMARIZING THE RESULTS AND CONCLUSIONS

135

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and Networking. CRC Press, 2011, pp. 1–585.

144 REFERENCES

145

List of Figures

Figure 2-1 Application scenarios for relaying in cellular systems ..................................... 9

Figure 2-2 Model of the relay-enhanced communication channel ................................... 10

Figure 2-3 Performance of the AF relay channel as a function of the RN feeder and sink

link SNRs ...................................................................................................... 12

Figure 2-4 Impact of self-interference on DF relay-enhanced channel capacity ............. 15

Figure 2-5 LTE-A relay-enhanced network model .......................................................... 17

Figure 2-6 Two-hop and multi-hop relaying topologies .................................................. 18

Figure 2-7 Hard frequency reuse scheme (reuse factor 3) ............................................... 19

Figure 2-8 Soft frequency reuse scheme (reuse factor 3) ................................................ 20

Figure 2-9 Fractional frequency reuse scheme (reuse factor 3) ....................................... 21

Figure 3-1 Resource allocation problem in a traditional (relay-less) system................... 27

Figure 3-2 Parameterization of the GBR utility function (wGBR

= 1) ............................... 37

Figure 3-3 Comparison of the ET and GBR utility functions .......................................... 38

Figure 3-4 GBR utility function of a conversational video service ................................. 39

Figure 3-5 Proportional fair GBR utility function of a conversational video service ...... 40

Figure 3-6 Delay-bounded utility function of a conversational video service ................. 41

Figure 3-7 Price of utility for a GBR satisfied delay-bounded conversational video

service............................................................................................................ 43

Figure 3-8 Two-hop relay-enhanced network model ....................................................... 44

Figure 3-9 RRM schemes for multi-hop RENs: (a) centralized, and (b) distributed ....... 53

Figure 4-1 In-band relaying operation ............................................................................. 60

Figure 4-2 LTE-A in-band backhaul/access multiplexing [58] ....................................... 61

Figure 4-3 LTE-A in-band relaying control information overhead [5, 59]...................... 61

Figure 4-4 Jain fairness index of resource allocation for RN- and BS-connected MSs:

(a) full dynamic range, (b) results for PF RRM ............................................ 65

Figure 4-5 Probability of reaching the MBSFN congestion with respect to the number of

RNs in an LTE-A REN ................................................................................. 66

Figure 4-6 Simulated resource allocation statistics of a two-hop in-band REN:

(a) expected allocation per MS, (b) Jain index over all MSs ........................ 68

Figure 4-7 Multi-hop relaying sub-network concept ....................................................... 69

Figure 4-8 Jain fairness index of multi-hop in-band relaying (β = 1) .............................. 71

Figure 4-9 Price of fairness of multi-hop in-band relaying .............................................. 73

Figure 4-10 Trading-off fairness and price of fairness of multi-hop in-band relaying

(β = 1) ............................................................................................................ 74

Figure 4-11 Simulated mean throughput vs. fairness characteristics of two-hop and

multi-hop in-band relaying ............................................................................ 74

Figure 4-12 Out-band RN configuration .......................................................................... 75

Figure 4-13 POF of out-band resource partitioning with respect to the number of system

carriers ........................................................................................................... 78

146 LIST OF FIGURES

Figure 4-14 Hybrid RN configuration .............................................................................. 79

Figure 4-15 POF of hybrid resource partitioning ............................................................. 80

Figure 4-16 Out-band RN BH link SINR degradation due to inter-carrier self-

interference coupling between adjacent carriers [58] .................................... 82

Figure 4-17 Comparison of in-band and out-band relaying performance at the same radio

conditions ...................................................................................................... 83

Figure 4-18 Unique MBSFN sub-frame patterns for two-hop relaying [53] ................... 86

Figure 4-19 Characteristic times of unique MBSFN sub-frame patterns for two-hop

relaying [53] .................................................................................................. 88

Figure 4-20 Expected in-band delay overhead for two-hop relayed links [53] ............... 89

Figure 4-21 Simulated transmission times for multi-hop in-band relaying with: (a) HD

IPTV, (b) SD IPTV, (c) audio streaming, and (d) online gaming traffic ...... 91

Figure 4-22 Simulated transmission times for various configurations of multi-hop

relaying with: (a) HD IPTV, (b) SD IPTV, (c) audio streaming, and

(d) online gaming traffic ................................................................................ 93

Figure 5-1 Inter-cell load balancing ................................................................................. 97

Figure 5-2 Intra-cell inter-carrier load balancing ............................................................. 97

Figure 5-3 Dynamics of radio conditions per CC ............................................................ 98

Figure 5-4 Probability density function of number of MSs per CC for the MH and the

RR carrier selection methods ...................................................................... 100

Figure 5-5 Simulated CC allocation to (a) RN BH and (b) RN AC links in an uniform

REN scenario ............................................................................................... 102

Figure 5-6 Simulated RN BH link statistics with various carrier selection methods in a

uniform REN scenario ................................................................................. 103

Figure 5-7 Simulated CC allocation to RN BH (sub-plots a,c) and RN AC (sub-plots b,d)

in test scenarios B (sub-plots a,b), and C (sub-plots c,d) ............................ 104

Figure 5-8 Simulated RN BH link statistics with various carrier selection methods in

evaluation scenarios with unmanaged interferes (sub-plots a,b), and BS SFR

ICIC (sub-plots c,d) ..................................................................................... 105

Figure 5-9 Resource allocation based on (a) FDM and (b) CA ..................................... 106

Figure 5-10 Carrier load characteristics with FDM and several levels of CA [59] ....... 109

Figure 5-11 Simulated resource availability vs. resource demand in a two-hop REN [55]

..................................................................................................................... 110

Figure 5-12 Simulated resource availability vs. resource demand of first-hop RNs in a

multi-hop REN [55] ..................................................................................... 111

Figure 5-13 AC SCC activation/deactivation function [26] ........................................... 113

Figure 5-14 Simulated RN AC activity with dynamic SCC adaptation ......................... 115

Figure 5-15 Simulated SINR improvement due to RN AC SCC adaptation ................. 116

Figure 5-16 Simulated CC allocation to RN BH links with long-term load-aware

adaptation [30] ............................................................................................. 118

LIST OF FIGURES 147

Figure 5-17 Simulated performance of multi-hop REN with long-term load-aware

adaptation [30]............................................................................................. 118

Figure 5-18 Interference coupling mechanisms in RENs [54]....................................... 120

Figure 5-19 REN ICIC dilemma [59] ............................................................................ 121

Figure 5-20 System status information collection for a centralized management ......... 125

Figure 5-21 Centralized coordination gains as a function of interference detection

sensitivities [59] .......................................................................................... 126

Figure 5-22 Simulated timeline of centralized ICIC process ......................................... 126

Figure 5-23 System status information exchange for a distributed management .......... 127

Figure 5-24 Comparison of centralized, distributed and autonomous ICIC schemes:

(a) at system start-up, and (b) at recovery from change in the network

deployment .................................................................................................. 129

Figure A-1 SINR-to-spectral efficiency mapping function (2x2 MIMO link) [78]....... 152

Figure A-2 RN and MS deployment models: (a) uniform, and (b) “hot zone” (ISD

1732 m) ....................................................................................................... 157

148 LIST OF FIGURES

149

List of Tables

Table 3-1 3GPP standardized QoS classes [12] ............................................................... 26

Table 3-2 Utility-optimal resource allocation procedure ................................................. 34

Table 4-1 3GPP standardized spectral transmitter/receiver characteristics [18-20] ........ 81

Table 5-1 Summary of carrier assessment metrics for RN carrier selection [29, 56] .... 101

Table 5-2 Short-term RN load-aware adaptation procedure [26] .................................. 114

Table 5-3 Long-term RN load-aware adaptation procedure [26]................................... 117

Table A-1 Mobile station model [2, 28] ........................................................................ 154

Table A-2 Base station model [2, 28] ............................................................................ 155

Table A-3 Relay node model [2, 28] .............................................................................. 156

Table A-4 Pathloss models [2, 28] ................................................................................. 159

Table A-5 Slow fading models [2, 28] ........................................................................... 160

150 LIST OF TABLES

151

Appendix A System Level Simulator Description

This appendix provides information about evaluation methodology and models used to

assess concepts discussed in this dissertation. This includes:

Simulation methodology, i.e., description of the modelling approach, modelled

mechanisms and assumed simplifications (see Appendix A.1),

Network model, i.e., definition of models of network nodes, deployment schemes

and user behaviour patterns (see Appendix A.2),

Traffic models, i.e., description of simulated traffic types and evaluation

scenarios (see Appendix A.3),

Propagation models, i.e., definition of models of radio links (see Appendix A.4).

In addition discussion of the reliability of the results of the simulations is conducted in

Appendix A.5.

A.1 Simulation Methodology

The evaluations presented in this dissertation are performed on the basis of computer

simulations of LTE-A relay-less networks and relay enhanced networks. The simulations

have been done on the system level, i.e., focusing on Layer-2 and Layer-3 procedures of

the system, with simplified physical layer (Layer-1) and core network processes. Only

the downlink transmission direction has been modelled.

Physical Layer Simplifications

Modeling of the physical layer is based on statistics collected from earlier link level

measurements and simulations [78]. Specifically, an SINR-to-spectral efficiency

mapping function is used that mimics behavior of the adaptive modulation and coding

(AMC), rank adaptation, precoding and the hybrid automatic repeat request (HARQ)

processes of the LTE-A Layer-1. This function estimates the expected value of the link

spectral efficiency at a specific SINR value. The employed mapping function is depicted

in Figure A-1. This function assumes 2x2 MIMO antenna configuration for all radio

links, with dual-stream transmission enabled.

152 APPENDIX A SYSTEM LEVEL SIMULATOR DESCRIPTION

Figure A-1 SINR-to-spectral efficiency mapping function (2x2 MIMO link) [78]

Estimation of the link SINR involves: (1) calculation of the signal power

coupling between the transmitting (Tx) and the receiving (Rx) nodes (see

Appendix A.4), and (2) estimation of the expected interference coupling with respect to

collision probability (i.e. having the same radio resource scheduled for the Tx and Rx

nodes). The collision probability is determined on the basis of the loads at the aggressor

and the victim cells (linear mapping assumed). In addition, 10% probability of collision

with control signals is assumed for all cells (based on estimations presented in [88] for a

10 MHz carrier with 2 Tx antennas at access points). The expected value of the link

SINR at specific load conditions is estimated using the exponential effective SINR

mapping (EESM) method. The EESM function is defined as [42]:

(A.1)

where is the SINR observed by the Rx node in relation to the Tx node , and is its

th random observation. The SINR

is calculated as:

subject to

(A.2)

where is the maximal signal power received by the Rx node from the Tx node (

is its th random observation), is the noise power, and is the load at the Tx node

in terms of resource utilization.

APPENDIX A SYSTEM LEVEL SIMULATOR DESCRIPTION 153

The physical layer simplification also takes into account Tx and Rx hardware

errors in form of:

receiver noise figure (NF) increasing the overall noise floor, i.e.:

(A.3)

where is the thermal noise power.

transmitter error in symbol formulation accounted for in form of the error vector

magnitude (EVM) [31], as in:

(A.4)

adjacent channel selectivity (ACS) and adjacent channel interference ratio

(ACIR) accounted for as defined in equations (4.27) and (4.29).

Layer-2/3 Procedure Modelling

Layer-2/3 procedures are mainly modelled in form of the initial and continuous resource

allocation processes and session management. The initial resource allocation includes:

DL power allocation – the total transmission power of every access point is

uniformly allocated to all utilized frequency sub-carriers,

Initial carrier allocation – the initial carrier selection for RNs’ BH and A links,

and for MSs’ serving links is done randomly according to the MH method (see

Section 5.2.2); BSs always use all the available frequency spectrum,

Resource partitioning configuration – MBSFN configuration and/or number of

backhaul/access CC selection for RNs is decided on according to the optimal

resource partitioning rule (see equation (3.59)) individually for each RN;

exception from this rule are the evaluations of the load balancing methods (see

Section 5.2.3), where this configuration is set the same for all RNs based on the

expected value of the relaying gain.

The continuous resource allocation includes:

Packet scheduling – time and frequency resource assignment to MSs and RNs

with respect to the iterative utility-based algorithm defined in Chapter 3;

resolution for this assignment is one physical resource block (PRB, 180 kHz) in

the frequency domain and one transmission time interval (TTI, 1 ms) in the time

domain,

Short term adaptation – activation and deactivation of RNs’ A S s according

to the procedure defined in Table 5-2,


Long term adaptation – reconfiguration of CC allocation for RNs and MSs

according to the procedures defined in Table 5-3 and/or in Section 5.3.2.

Session management includes: data packet creation and flow control, time to live (TTL)

control and expired packed deletion, and buffer management appropriately to the traffic

model used by an MS.

A.2 Network Model

The simulated networks include three types of nodes: BSs, MSs and RNs. Detailed

specification of the three types of nodes is given in Table A-1 for MSs, in Table A-2 for

BSs and in Table A-3 for RNs.

Table A-1 Mobile station model [2, 28]

Parameter Description

Maximum

transmission power

Noise figure

Error vector

magnitude

Antenna model 1 transmission + 2 reception antennas

Isotropic antenna with antenna gain:

Deployment Random uniform over the whole simulated area

Random with respect to a density map

Antenna height


Table A-2 Base station model [2, 28]


Transmission power

spectral density

Noise figure

Error vector

magnitude

Antenna model

2 transmission + 2 reception antennas

;

;

;

Deployment

Regular placement on a hexagonal grid

with inter-site distance (ISD):

500 m (urban scenario)

1732 m (sub-urban scenario)

Each BS site supports three sectors with horizontal antenna directions

of: , and

Vertical antenna direction:

in the urban scenario

in the sub-urban scenario

Antenna height

is the BS antenna function (directional antenna)

is horizontal AoD/AoA with respect to the main horizontal antenna direction,

is vertical AoD/AoA with respect to the main vertical antenna direction,

is the BS antenna gain over an isotropic antenna

is the BS horizontal antenna attenuation pattern

is the BS vertical antenna attenuation pattern

is the BS maximum antenna attenuation

is the BS vertical pattern side lobe attenuation


Table A-3 Relay node model [2, 28]


Downlink

transmission power

spectral density

Uplink maximum

transmission power

Noise figure

Error vector

magnitude

BH antenna model

2 transmission + 2 reception antennas

;

;

AC antenna model 2 transmission + 2 reception antennas

Deployment Planned along base station cell borders [28] (see Figure A-2)

Antenna height

Buffer size 1 Mbit

is the RN BH antenna gain function (directional antenna)

is horizontal AoD/AoA with respect to the main horizontal antenna direction,

is vertical AoD/AoA with respect to the main vertical antenna direction,

is the RN BH antenna gain over an isotropic antenna

is the RN BH horizontal antenna attenuation pattern

is the RN BH maximum antenna attenuation

is the RN AC antenna gain (omnidirectional antenna)

In the evaluations a regular network of tri-sectorised BSs is considered (see

Figure A-2). In addition the “wrap-around” technique [41] is used to avoid edge effects

on the boundary sectors of the network. For RN and MS deployment two options are

used: (1) uniform and (2) “hot zone”. In the uniform scenario MSs are deployed

randomly with uniform probability and RNs are deployed along edges of sector borders

(see Figure A-2a). In the “hot zone” scenario a number of MSs is deployed randomly in

a predefined circular area with an RN deployed in its centre (see Figure A-2b).


Figure A-2 RN and MS deployment models: (a) uniform, and (b) “hot zone” (ISD 1732 m)

BSs and RNs are stationary, MSs move in the network with velocity of 3 km/h.

In the uniform deployment scheme movement of MSs is random and unrestricted (with

“wrap around” used if a MS exits the simulated area). In the “hot zone” scenario

movement of the MSs is restricted to the designated area of the “hot zone”.

RNs select their respective donor nodes and MSs select serving nodes based on

the highest received signal power criterion. If multi-hop relaying is enabled, there is a

limit on the maximum number of 5 end-to-end hops (assumption based on the study

described in [52]). MSs execute the cell-reselection procedure [3] while moving in the

simulated area. An MS handover is executed if a signal is received from a non-serving

cell with power at least 3 dB higher than the signal power received from the serving cell.

A.3 Traffic Models

In the evaluations three traffic types are simulated:

1. Full buffer – elastic traffic with an infinite data payload;

2. Bursty traffic [2] – data rate elastic traffic with payload size of 0.5 MB; after

finalized transmission of the data payload an MS deactivates itself for a random

“reading” time ; the “reading” time is characterised with the exponential

distribution (A.5) with 5 s mean value;

(A.5)

after the “reading” time the MS activates itself with a new data payload; the

maximum transmission time for the data payload is 300 ms.


3. Streaming traffic – data rate and delay bounded traffic; four services are possible

for this traffic type [12, 36, 96]:

3.a. HD video: 7.5 Mbit/s GBR, 130 ms RAN TTL, 1374 B packets,

3.b. SD video: 2.5 Mbit/s GBR, 130 ms RAN TTL, 1374 B packets,

3.c. Audio: 0.32 Mbit/s, 80 ms RAN TTL, 1374 B packets,

3.d. Online gaming: 0.04 Mbit/s, 30 ms RAN TTL, 130 B packet size.

Number of MSs is constant during simulation time. Each MS is characterized with only

one traffic type. Evaluations are conducted with either all MSs using the same traffic

type or with mixtures of the above defined services.

A.4 Propagation Models

Each radio link is modelled according to general formula (A.6). The formula defines the

received signal power as a function of:

Transmitted signal power

Antenna gains of the transmitter ( ) and the receiver ( ) (according to

antenna models defined for appropriate nodes in Appendix A.2)

Link attenuation related to signal propagation on the path from the transmitter to

the receiver (pathloss, )

Gaussian distributed random slow fading ( ) related to large scale obstacles

and shadowing effects

Rayleigh distributed random fast fading ( ) related to multipath propagation

and Doppler effects

(A.6)

In formula (A.6) all values are given in the decibel scale. The signal power

calculations are done with respect to a single PRB bandwidth, i.e., 200 kHz (including

guard bands).

Table A-4 presents summary of pathloss models for all possible links in an REN,

i.e., the direct BS-MS link ( ), BS-RN backhaul link ( ), RN-MS access link

( ) and RN-RN link ( ). The RN-RN link acts as the inter-RN-cell interference

link or as RN BH link in case of multi-hop relaying. For all link types the pathloss is

defined in form of two components: line of sight (LOS) and non-line of sight (NLOS).

For each link one of the components is selected randomly with respect to a distant-

dependent LOS probability function . The formulas for the are defined

separately for the urban and the sub-urban scenarios.


Table A-4 Pathloss models [2, 28]


Direct

BS-MS link

Urban scenario (ISD 500 m):

Sub-urban scenario (ISD 1732 m):

RN-MS

access link

Urban scenario (ISD 500 m):

Sub-urban scenario (ISD 1732 m):

BS-RN

backhaul link

Urban scenario:

Sub-urban scenario:

RN-RN

multi-hop

link

Urban scenario:

Sub-urban scenario:

is the link distance in meters

is the carrier frequency of the transmission

Table A-5 presents parameters of the slow fading experienced by the radio links.

For each link type two basic parameters are specified: standard deviation and

decorrelation distance . The standard deviation defines the strength of the fading and


the decorrelation distance defines the rate of change of the fading with distance. In

addition it is assumed that:

Links to sectors of one BS site experience the same slow fading value.

Links to different BS and RN sites experience slow fading with 50% correlation

(i.e. in simulation one common slow fading map is generated and one specific

slow fading map for each BS and RN site, the resultant slow fading value for a

link is an average of the common fading value and the site-specific fading value).

Slow fading experienced by an RN on BH link to a BS is fully correlated with

the fading experienced by the MSs at the RN position on the direct link to the

same BS.

Slow fading is only applied to NLOS type of links.

Table A-5 Slow fading models [2, 28]


Direct BS-MS link ;

RN-MS access link ;

BS-RN backhaul link ;

RN-RN link ;

Fast fading is modelled as a 0 dB mean random Rayleigh process correlated in

time. Coherence time ( ) of the process is defined as (A.7).

(A.7)

where is the speed of light and is velocity of the receiver in relation to the

transmitter.

A.5 Results Reliability Discussion

All simulations conducted as part of this research work are Monte Carlo simulations.

Evaluation of each scenario involved 20 random network realizations. The randomness

of a network realization included:

MSs locations – selected randomly according to a predefined deployment

scenario (see Figure A-2), additionally the MSs were moving with velocity of

3 km/h, movement paths of the MSs were also random,


RN locations – selected randomly within 15 m radius from the positions defined

in the deployment scenario (see Figure A-2),

shadow fading and line-of-sight propagation conditions selected randomly

according to models defined in Appendix A.4,

additional elements dependent on the simulated scenario (e.g. deployment of

interfering femto cells in Chapter 5 simulations).

In each network realization 21 BS cells with 0-12 (typically 10) RNs per BS cell were

modelled. The “wrap-around” [41] technique was used to avoid edge effects, thus, data

could be collected from all BS cells. This means that for a typical simulation with 10

RNs deployed per BS cell, RN performance statistics were collected from 210 RNs per

network realization, 4 200 RNs per simulation scenario.

Depending on the selected traffic scenario various numbers of MSs were

modelled per network realization (see Appendix A.3 for definitions of the traffic types):

full buffer scenario – 25 MSs per BS cell, i.e. 525 MSs per network realization,

10 500 MSs per simulation scenario,

heterogeneous traffic scenario – various numbers of MSs used depending on the

assumed target system load, but never less than 50 MSs per BS cell, i.e. 1 050

MSs per network realization and 21 000 MSs per simulation scenario.

With the biggest BS cell size setting (1732 m BS inter-site distance, i.e. ≈865 m2 BS cell

area) this means at least 12 data collection points per 1 m2 of a BS cell were used

(calculated for the full buffer scenario). In addition the MSs’ movement was enabled in

the RRM coordination (i.e. Chapter 5) simulations. The MSs’ movement allowed data

collection from multiple locations per simulated MS. In those simulations 10 s of

network operation time was simulated with MS position update every 800 ms (≈0.67 m

step), which results in 12 positions per simulated MS.

Simulated network operation time was at least 4 s and the resource assignment

was done every 1 ms. First 2 s of network operation time were considered as the warm-

up period, i.e. resource allocation and data rate statistics were not collected during this

time. Resource allocation and data rate statistics were averaged in time over the data

collection period.

In the authors opinion the simulation configuration described above provides

sufficient amount of independent data samples to claim that the results presented in this

dissertation are statistically representative for the simulated scenarios and processes.

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