WINLAB
Dynamic Spectrum Access in 5G
Narayan B. Mandayam
WINLAB, Rutgers [email protected]/~narayan
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WINLAB
What is 5G ?
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Wide range of spectrum choices
100s of MHz to 100 GHz,
Flexible BW, Licensed, Unlicensed
Wide range of application choices
IoT, M2M, D2D
V2V
Wide range of QoS requirements
Ultra low latency
Very high data rate, Best effort
Wide range of device choices
Low power, Mid-to-high power
Low complexity, High complexity
Wide range of networking choices
Mesh, Capillary, Phantom, HetNets
5G: Anything you want it to be!
5G: Academic’s dream!
Wide range of networking paradigms
ICN, MF, NOM, User-centric
WINLAB
5G DSA: What’s out there ?
Three distinct approaches to DSA have been proposed
Agile/cognitive radio – autonomous sensing at radio devices to avoid interference
Spectrum Access System (SAS) – centralized Database to provide visibility of
potentially interfering networks and/or global assignment
Distributed inter-network collaboration – peering protocols to support decentralized
spectrum assignment algorithms
AP/ BS
A
AP/ BS
B
Net ARF sensing
RF sensing
Spectrum
Server
Net B
Net C
Distributed
Algorithm
1. AGILE RADIO
Internet
Query/
Assignment
2. SPECTRUM SERVER
3. DECENTRALIZED
NETWORK COLLABORATION
(Collocated Networks)
WINLAB4
5G DSA: Agile radio
Cognitive radio networks require a large of amount of network (and channel) state information to enable efficient
Discovery, Self-organization
Resource Management
Cooperation Techniques
PHY A
PHY B
PHY C
Control
(e.g. CSCC)
Multi-mode radio PHY
Ad-Hoc Discovery
& Routing Capability
Functionality can be quite
challenging!
Scalability?
Cost of Cooperation?
WINLAB
5G DSA: Spectrum Access System (SAS)
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Primarily in 3.5 GHz spectrum
Small Cells for Cellular
Coexistence with Navy Radar
Internet
Query/
Assignment
SPECTRUM SERVER
Design Principles and Architecture
Registration with Spectrum Server/Database Tiering and Prioritization of users Protect Incumbents Wide range of technical issues related to access
Licensed Shared Access Generalized Authorized Access
Control and Network State Information Radio and Network parameters exposed
Coordination across databases Monitoring and Enforcement
WINLAB
5G DSA: Network Cooperation
Net A
Net B
Net C
Distributed
Algorithm
Radio MAP
Information
Exhange
SAVANT: Spectrum Access Via Inter-Network Cooperation
Focus on decentralized architecture for sharing spectrum info
Parallels with BGP exchange of route information between peers
Architecture enables regional visibility for setting radio parameters
Further, networks may collaborate to carry out logically centralized
optimization for max throughput subject to policy/technology constraints
Local Adaptation to
Observed Spectrum UseCooperative Regional
Optimization of Radio
Parameters
*Supported by NSF EARS grant CNS 1247764
WINLAB/Princeton Project
WINLAB
SAVANT: Inter-Network Protocol
Architecture involves two protocol interface levels between
independent wireless domains:
• Lower layer for sharing aggregate radio map using technology neutral
parameters
• Higher layer for negotiating spectrum use policy, radio resource
management (RRM) algorithms, and controller delegation
WINLAB
Elephant in Room: WiFi
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Smart Phone growth is the U.S. from 2013 to 2015 is ~300% Smartphone data consumption in 2015 ~10 GB/user/month
~85% over WiFi and ~15% over Cellular
WiFi AP density in cities ~100-200 per sq km
01/2009 01/2010 01/2011 01/2012 01/20130
5
10
15
20
25
Date
% o
f E
nte
rpri
se
/SP
AP
s
San Francisco
New York
Chicago
Boston
Licensed Assisted Access (LAA) and other cooperative methods including aggregation/integration with WiFi
WINLAB
5G DSA: Technical Challenges
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Noncontiguous Spectrum Transmission TX power is no longer “King”!
Control Plane Design Scalability, Performance
Distributed/Hybrid Algorithms for Spectrum Coordination
Stability, Convergence of Algorithms
Case for Noncontiguous Transmission - I
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1
2
3
A
B
C
X
• Three available channels
• Node A transmits to node C via node B.
• Node B relays node A’s data and transmits itsown data to node C.
• Node X, an external and uncontrollableinterferer, transmits in channel 2.
2
If we use max-min rate objective and allocate channels, node B requires two channels; node A requires one channel
Scheduling options for Node A and Node B?
?
?
Case for Noncontiguous Transmission - II
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2
A
C
3
B
• Transmission in link BC suffers interference in channel 2
1 2
#1: Contiguous OFDM
X
2
A
C
B
• Spectrum fragmentation limited by number of radio front ends
1 3
2
#2: Multiple RF front ends
X
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2
A
C
B
2
1 3
#3: Non-Contiguous OFDM (NC-OFDMA)
Nulled Subcarrier
X
NC-OFDM accesses multiple fragmented spectrum chunks
with single radio front end
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2
A
AP
B
2
1 3
Non-Contiguous OFDM
Nulled Subcarrier
Serial toParallel
IFFTParallelto Serial
D/A
X
X[1] X[3]X[1]
X[3]
0
x[1]x[2]x[3]
X[2] =
NC-OFDM accesses multiple fragmented spectrum chunks
with single radio front end
• Node B places zero in channel 2 and avoids interference
• Node A, far from the interferer node X, uses channel 2.
• Both nodes use better channels.
• Node B spans three channels, instead of two.• Sampling rate increases.
Modulation
NC-OFDM Operation
Resource Allocation in Noncontiguous Transmission
Benefits: Avoids interference, incumbent users
Uses better channels
Each front end can use multiple fragmented spectrum chunks
13
Challenges: Increases sampling rate
Increases ADC & DAC power Increases amplifier power
Increases peak-to-average-power-ratio (PAPR)
Multiple RF Front Ends vs Single RF Front End ?
Centralized, Distributed and Hybrid algorithms for carrier and forwarder selection, power control ?
Spectrum Allocation under Interference and Spectrum Span Constraints
Radio nodes
Interference nodes
Available channels
Controller
How to allocate noncontiguous channels subject to ADC/DAC power constraints?
Maxmin Rate Allocation (Integer Linear Program)
n1 n2 n3
n4 B n5
n6 n7 n8
CA
L1 n1-n2
L2 n3-n4
L3 n5-n7
L4 n6-n8
WINLAB
Control Plane Design: Noncontiguous Transmission
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CDMA is back!
Short PN-seq Control Channel DataLong PN-seq
Experimental Results from ORBIT testbed
Network Setup:
• Multiple p2p secondary links operating in the presence of a primary transmission
• 1 MHz BW, 64-subcarrier NC-OFDM with CDMA-based underlay (spreading sequence length 40-160)
• Underlay to noise ratio ~ 0 dB, primary transmission to noise ratio ~ 10 dB
ORBIT testbed
USRP
Result 1: Spectrum assignment while minimizing spanof assigned subcarriers (reduces ADC/DAC powerconsumption)
Reassigned subcarriers with minimal loss (< 10%)of throughput
Result 2: Reliable timing and frequency recovery fromunderlay control channel in the presence of primarytransmissions Result 3: Control channel BER as a
function of primary signal strength withunderlay to noise ratio set to 0 dB;Control channel rate = 30 kbps
Primary Signal SNR BER
3 dB < 1e-3
6 dB 6.3*1e-3
7.7 dB 2.6*1e-2
9.2 dB 9.2*1e-2
correct timing instance
peak indicating timing instance
detection
peak detection threshold
WINLAB
Conventional LTE Conventional Wi-Fi
Spectrum Exclusive licensed Shared unlicensed
Operation
technique
OFDMA: channel hopping over
time to exploit good channel
condition
CSMA/CA: Channel sensing
before transmission to avoid
packet collision
Controller
entity
A single licensed carrier No common controller
Advantage Packet efficient Cost effective, fair sharing
Network Coordination: LTE/WiFi
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WINLAB
Formulating LTE/WiFi Cooperation as an Optimization problem
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LWiP
LWiPP
W, i NGPGP
LrS
WirS
SS
i
i
C
Lj
ijj
M
ikk
jjll
iiww
Lj
jl
Wi
ba
iw
bi
liiw
, , : variablesgControllin
, , 0
,
,j ,)log1(
, ,)log1( subject to
11 maximize
max
0
k
min,2
min,2
Objective: Downlink power control optimization using Geometric
ProgrammingMaximize sum-throughput across Wi-Fi and LTE
Minimum SINR requirement for data rate transmission
CCA threshold requirement at Wi-Fi
Range of Tx power
Tx power
WiiMMb
WiiMMa
S
b
i
b
ii
a
i
a
ii
i
, : ,||11
, : ,||11
:
i l ink at SINR
where
Set of Wi-Fi APs in the CSMA range of AP
Set of Wi-Fi APs in the interference range of AP
WINLAB
LTE/WiFi Scenario
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Interfering
APj
Associated
APi
Interfering
APj
dA
-| dI| +|
dI|
(0,0)
UEi
+x-axis- x-axis
• UE – Associated AP: either Wi-Fi or LTE link, interfering AP is of
other technology
• Varying parameters:
• dA = distance(UE, Associated AP)
• dI = distance(UE, interfering AP)
• Assuming UE at (0,0): if interfering AP on the (1) –X axis, dI = -| dI|,
(2) +X axis, dI = +| dI|
• Reason: inter-AP distance matters due to WiFi clear channel assessment
WINLAB
Example LTE/WiFi Coordination Results –Performance of LTE
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20 40 60 80 100-100
-50
0
50
100
AP-UE dist [m]
Inte
rfering A
P-U
E d
ist [m
]
10
20
30
40
50
60
20 40 60 80 100-100
-50
0
50
100
AP-UE dist [m]
Inte
rfering A
P-U
E d
ist [m
]
10
20
30
40
50
60
20 40 60 80 100-100
-50
0
50
100
AP-UE dist [m]
Inte
rfering A
P-U
E d
ist [m
]
10
20
30
40
50
60
20 40 60 80 100-100
-50
0
50
100
AP-UE dist [m]
Inte
rfering A
P-U
E d
ist [m
]
10
20
30
40
50
60
No coordinationPower control optimization Time division channel access optimization
Sagari, Baystag, Saha, Seskar, Trappe & Raychaudhuri, “Coordinated Dynamic Spectrum Management of LTE-U and WiFi Networks”
IEEE Dyspan 2015 (to apear)
WINLAB
20 40 60 80 100-100
-50
0
50
100
AP-UE dist [m]
Inte
rfering A
P-U
E d
ist [m
]
10
20
30
40
50
60
20 40 60 80 100-100
-50
0
50
100
AP-UE dist [m]
Inte
rfering A
P-U
E d
ist [m
]
10
20
30
40
50
60
20 40 60 80 100-100
-50
0
50
100
AP-UE dist [m]
Inte
rfering A
P-U
E d
ist [m
]
10
20
30
40
50
60
Example LTE/WiFi Coordination Results: Performance of WiFi
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20 40 60 80 100-100
-50
0
50
100
AP-UE dist [m]
Inte
rfering A
P-U
E d
ist [m
]
10
20
30
40
50
60
No coordinationPower control optimization Time division channel access optimization
Sagari, Baystag, Saha, Seskar, Trappe & Raychaudhuri, “Coordinated Dynamic Spectrum Management of LTE-U and WiFi Networks”
IEEE Dyspan 2015 (to apear)
WINLAB
End-User Behavior and Radio Resource Management
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Differentiated Pricing
How does uncertainty in the service affect end-user decisions and the network?
Increasing significance of end-user decisions
Can we influence end-user behavior and improve RRM?
Higher speed
Low
er g
uara
nte
e
Figure from www.fcc.gov Measuring Broadband America
WINLAB
Prospect Theory: An Alternative to Expected Utility Theory for Modeling Decision Making
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Losses usually “loom larger” than gains
Probability Weighting Effect
Framing Effect “Overweigh” low probabilities
“Underweigh” moderate and high
probabilities
WINLAB
Prospect Pricing for Radio Resource Management
User preferences, biases can be
“mitigated” by pricing
Can be used to improve RRM
Under EUT, loss is 0
Deviation from EUT results in
loss, pricing reduces loss
25Yang, Park, Mandayam, Seskar, Glass and Sinha “Prospect Pricing in Cognitive Radio Networks” IEEE Trans. on Cognitive Communication Networks, To Appear
Psychophysics Experiments
Measured Probability Weighting
Function for video QoS
WINLAB
Rural Broadband: LTE-U based Backhaul in TVWS with Local WiFi Access
WiFi Coverage Area
WiFi Coverage Area
WiFi Coverage Area
WiFi Coverage Area
WiFi Coverage Area
Backhaul Tower with
WS Radio and WiFi AP for
local distribution
Backhaul Tower with
WS Radio and WiFi AP for
local distribution
WiFi Coverage Area
WiFi Coverage Area
Tower with Fiber Access
LTE-U Link
BS 5
BS 1
BS 2 BS 3
BS 4
BS 6
BS 7
LTE-U BS 1 Coverage
Area
LTE-U BS 6 Coverage Area
WiFi Coverage Area
BS 8
LTE-U BS 4 Coverage
Area
LTE in TVWS: FCC Guidelines
LTE Attributes FCC TVWS Rules for 6 MHz Channel
LTE eNodeB DL Transmitter Power
2W EIRP for LTE FDD 3 MHz
LTE eNodeB UL Transmit Power
2W EIRP for LTE FDD 3 MHz
LTE eNodeB Transmitter Height
30 meters HAAT
LTE eNodeB Antenna Gain 0 dBi
LTE in TVWS: Simulation throughputs with multiple channels
0
50
100
150
0 5 10 15 20
Thro
ugh
pu
t (M
bp
s)
Distance (km)
LTE FDD Throughput with multiple TVWS channels vs Inter-Tower Distance DL TP @ 1 TVWS
ChanDL TP @ 2 TVWSChanDL TP @ 3 TVWSChanDL TP @ 4 TVWSChanDL TP @ 5 TVWSChanDL TP @ 6 TVWSChanDL TP @ 7 TVWSChan18 Mbps Load
35 Mbps Load
Estimated RuralDemandMean Estimate ofRural Demand
Generic Scenario : E.g. Wichita, KS
• Area: 423 square km2
• Population: 385,577 (2012 Census) [1]
• Available white space for fixed devices [2]
57 79 85 491 527533 671
Location(MHz)
Maxmin Rate Backhaul
11.72
26.36
46.86
15.98 15.98 15.98
36.52 36.52 36.52
60.87 60.87
54.78
91.31 91.31
85.02
0
10
20
30
40
50
60
70
80
90
100
lnter-tower distance = 2 Km lnter-tower distance = 3 Km lnter-tower distance = 4 Km
Dat
a R
ate
(Mb
ps)
Throughput vs Demand for Various Cell Size
Traffic Demand A = {5} A = {1,9} A = {1,5,9} A={1,3,7,9}
3 Fiber BS can cover 144 sq km
References• R. Kumbhkar, T. Kuber, G. Sridharan, N. B. Mandayam, I. Seskar,
“Opportunistic Spectrum Allocation for Max-Min Rate in ” DySPAN 2015, October 2015
• S. Sagari, Baystag, D. Saha, I. Seskar, W. Trappe, D. Raychaudhuri, “Coordinated Dynamic Spectrum Management of LTE-U and WiFi Networks” DySPAN 2015, October 2015
• S. Pattar, N. B. Mandayam, I. Seskar, J. Chen, Z. Li, “Rate Optimal Backhaul and Distribution using LTE in TVWS” SCTE Cable-Tec Expo’15, October 2015
• R. Kumbhkar, M. N. Islam, N. B. Mandayam, I. Seskar, “Rate Optimal design of a Wireless Backhaul Network using TV White Space,” COMSNETS 2015, January 2015
Y. Yang, L. Park, N. B. Mandayam, I. Seskar, A. Glass and N. Sinha “Prospect Pricing in Cognitive Radio Networks” IEEE Trans. on Cognitive Communication Networks, To Appear
31
Acknowledgments
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• U.S. National Science Foundation
• Office of Naval Research
• WINLAB Collaborators: Ratnesh Kumbhkar, Gokul Sridharan, Neel Krishnan, Ivan Seskar, Dipankar Raychaudhuri, Arnold Glass
• Qualcomm: Nazmul Islam
• NRL: Sastry Kompella