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電機工程學系
1
Evolution to 5G Wireless
with Mobile Cloud Applications
Li-Chun Wang
王蒞君
National Chiao Tung University,
Dept. of Electrical and Computer Engineering
Hshinchu, Taiwan
Outline
• Generation aspects
• Drivers for 5G Wireless
– Radio aspect
– Access aspect
– Network aspect
– Service aspects
– Terminal aspect
• Conclusion
2
Toward 4G Mobile Communications
3/19
2G 3G WCDMA
14.4Kbps 2Mbps
4G LTE/LTE-A
Unlimited
Possibility
~1Gbps
3.5G HSPA
14.4Mbps
HSPA: High Speed Packet Access
LTE: Long Term Evolution3
Applications for 5G
7
3D, Mobile
Augment Reality
YouTube
Web, Video
SMS
Source: Josef Noll, University of Oslo 4
4G Definition by ITU
IMT-Advanced (4G)
• Data rate > 100Mbps, mobile
• Data rate > 1Gbps, nomadic
3G 3.5G 4G 5
Key Features of IMT -Advanced
• a high degree of commonality of functionality worldwide while
retaining the flexibility to support a wide range of services and
applications in a cost efficient manner;
• compatibility of services within IMT and with fixed networks;
• capability of interworking with other radio access systems;
• high quality mobile services;
Note: IMT = International Mobile Telecommunications
6
Key Features of IMT-Advanced
• user equipment suitable for worldwide use;
• user-friendly applications, services and equipment;
• worldwide roaming capability;
• enhanced peak data rates to support advanced services
and applications (100 Mbit/s for high and 1 Gbit/s for low
mobility were established as targets for research).
67
Outline
• Generation aspects
• Drivers for 5G Wireless
– Radio technology aspect
– Access aspect
– Network aspect
– Service aspects
– Terminal aspect
• Conclusion
98
Radio Technology Aspect for 5G
• 2001
– CDMA/TDMA
• 2005
– OFDMA
– MIMO
• 2010s
– Network MIMO
» CoMP in 3GPP LTE-A
» Co-MIMO in WiMaX
9
Question?
• 1G (FDMA),
• 2G (TDMA/CDMA),
• 3G (WCDMA),
• 4G (OFDMA),
• Q. How about radio techniques for 5G?
• Ans: We may see many “integrated” multiple access
technologies since no access techniques are perfect.
– OFDMA is good for high data rates, but not good for inter-
cell interference and low transmission power
– CDMA is good for canceling inter-cell interference, but not
scalable for high data rate transmission
10
Research Trends in Wireless Networks
• The Past Two Decades: Key Developments at the Link Level :
– MIMO; MUD; Turbo
• Today: An Increased Focus on Interactions Among Nodes
– Competition
» Cognitive radio
» Information theoretic security
» Game theoretic modeling, analysis & design
– Collaboration
» Network coding
» Cooperative transmission & relaying
» Multi-hop transmission & coalition games
» Collaborative beam-forming
» Collaborative inference
– Competition & Collaboration in Wireless Networks
11
Observation 1
• What other degree of freedom (d.o.f.) can we use to
improve the performance of wireless systems?
– Besides the d.o.f. in power, time, frequency, and space
– The d.o.f. in the USER domain is the direction that we should think again.
» Select the right user to user, e.g., multiuser
scheduling
» Sharing channel information, e.g. Interference
alignment (d.o.f. = MK/2, where M = no. of antennas;
K = no. of users)
12
Observation 2
• How to turn “multiple users/base stations/systems” into the
advantages of wireless systems?
– Key enabling techniques cooperative wireless
systems from different aspects, such as antenna, users, base stations, and systems
13
Hierarchical (Heterogeneous) Cellular
Architectures in 4G LTE
Bring Transmitters Closer to Users
• Capacity enhancement
• Power saving
• Interference reduction
• Coverage extension
14
15
What and Why Network MIMO?
• Inter-cell interference: In conventional cellular systems, the solution is
using larger frequency reuse among cells
– Poorer spectral efficiency!!
CSI exchange through high-speed backbone
MIMO broadcast system
Network MIMO system
Personalized data transmissions
Inter-stream interference free!!
Inter-cell interference free!!
The concept is also used in LTE-A (CoMP) and 16m (Co-MIMO)
CoMP (Coordinated Multipoint Transmission and Reception)
• DL CoMP Transmission Scheems
– Joint Processing
» Joint transmission/dynamic cell selection
– Coordinated Scheduling/Beamforming
• Better cell edge performance
– Less co-channel interference / more signal
11 16
4 Scenarios for CoMP in 3GPP LTE-A
• According to R1-110564 in 3GPP, CoMP (coordinated
multipoint transmission) techniques are applied in the
following 4 scenario
eNB
Coordination area
High Tx
power RRH
Assume high Tx power RRH
as same as eNB
Optical fiber
Low Tx power
RRH
(Omni-antenna)
eNB
Optical fiber
Scenario 3 : Heterogeneous network with low power
RRHs, which create cells with their own cell ID.
Scenario 2 : Homogeneous network with high Tx power
RRHs
Scenario 1 : Homogeneous network with intra-site
Low Tx power
RRH
(Omni-antenna)
eNB
Optical fiber
Scenario 4 : Heterogeneous network with low power
RRHs, which create cells with the same cell ID as the macro.
17
18
key Issues for Network MIMO
• Issue 1: How many cells should be coordinated for a
network MIMO system?
• Issue 2: Inter-group interference (IGI) issue
G0
0
1
2
3
4
5
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G1
G2
G3 G4
G5
G6
G8
G9
7
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0
1
2
3
4
5
6
G0
G3
G4
G5
G1
G2
G6
3-cell Network MIMO 7-cell Network MIMO
1st-tier neighboring cells
2nd-tier neighboring cells
IGI causes serious signal
quality degradation
-30 -20 -10 0 10 20 30 40 50 600
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Received SINR (dB)
CD
F
7-cell coord. (with IGI)
3-cell coord. (with IGI)
3-cell coord. (no IGI)
19-cell coord. (no IGI)
7-cell coord. (no IGI)
Omni-cell with reuse one
14 dB degradation for 7-cell network MIMO
23 dB degradation for 3-cell network MIMO
19
3-Cell Network MIMO architectures with
sectorization and fractional frequency reuse
[JSAC’11]
Rearranged tri-sector frequency partition can achieve More efficient
coordination between multi-base stations
1B
2B
3B
3B
1B
2B
0
1
2
3
4
5
6
7
8
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3B
2B2B
3B
2B
3B
2B
3B
1B
3B
2B
2B
3B
2B
3B
2B
3B
1B
2B
3B
1B
2B
3B
2B
3B
2B
3B
3B
2B
2B
3B
3B
2B
2B
3B
3B
2B
Considered frequency band
Coordinated cells
Interferers by directional antenna
Interferers by side lobe
1B
1B
1B
1B 1B
1B1B 1B
1B
1B 1B
1B
1B
1B
Three kinds of frequency
partition for cell
2B
3B
1B
3B
1B
2B
1B
2B
3B
Coordinated group under
certain frequency band
FFR: fractional frequency reuse
20
Fractional Frequency Reuse (FFR) in Tri-Sector Cell*
(Regular Approach)
1B
2B
3B
1B
2B
3B
1B
2B
3B1B
2B
3B
1B
2B
3B
1B
2B
3B
1B
2B
3B
0
1
2
3
4
5
6
Sector cell-1
Sector cell-2
Sector cell-3
Frequency
Power level
Total bandwidth
Each cell has the same frequency partition!!
*F. Khan, LTE for 4G Mobile Broadband: Air Interface Technologies and Performance, 1st ed. Cambridge University Press,
2009.
21
Regular versus Rearranged Frequency Partitions
1B
2B
3B
3B
1B
2B
0
1
2
3
4
5
6
3B
1B
2B
2B
3B
1B
2B
3B
1B
3B
1B
2B
2B
3B
1B
1B
2B
3B
1B
2B
3B
1B
2B
3B
1B
2B
3B
1B
2B
3B
1B
2B
3B
1B
2B
3B
0
1
2
3
4
5
6
Network MIMO group for
Network MIMO group for
Network MIMO group for
N resource units (RU)
Frequency
Power level
Cell iG
a
Cell iG
b
Cell iG
c
Regular Rearranged
A cell can simultaneously forms 3 groups with neighboring 6 cells
22
Benefit of Rearranged Frequency Partition
• Extra 8.5 dB SINR improvement!!
(compared to traditional tri-sector FFR)
-20 -10 0 10 20 30 400
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Received SINR (dB)
CD
F
Omni-cell with reuse one
ZF-DPC network MIMO in
rearranged frequency partition
ZF-DPC network MIMO in
regular frequency partition
Sectoring (1/3) FFRExtra 2.7 dB gain under regular
partition + 3-cell network MIMO
Extra 8.5 dB gain under rearranged
partition + 3-cell network MIMO
10 dB improvement for sectoring FFR
90% percentile
23
Benefit of Proposed Architecture
over Conventional Approach
• A smaller coordination, 3-cell network MIMO architecture, can even
outperform conventional 7-cell network MIMO system
– 2 dB SINR improvement for 60o sectoring
– 3 dB SINR improvement for 120o sectoring
-20 -10 0 10 20 30 40 500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Received SINR (dB)
CD
F
3-cell omni-coordination
(with IGI)
7-cell omni-coordination
(with IGI)
Diamond: FFR-based 3-cell network
MIMO under 120o-shaped cell
Square: FFR-based 3-cell network
MIMO under 60o-shaped cell
Traditional 7-cell approach
Proposed 3-cell approach
with rearranged partition
24
Summary
• By exploiting geographic cell distribution and frequency
partition, a 3-cell network MIMO can outperform
conventional 7-cell network MIMO with omni-directional cell
• This kind of 3-cell coordinated network MIMO architectures
is particularly useful
– The default number of neighboring cells in the Co-MIMO
transmission is three for IEEE 802.16m
– In LTE-A RAN 1 meeting, the number of coordination in
CoMP is also three
CoMP: coordinated multi-point
CoMIMO: collaborative multiple-input multiple-output
Other B4G radio technologies
– Hybrid Multiple Access (HMA)
» Proper combining CDMA and OFDMA greatly
improves the performanceOn Hybrid Multiple Access and Its Reuse Partitioning, IEEE Globecom 2010
On the Design of Comb Spectrum Code for Multiple Access Scheme, IEEE Tran. On Comm. ,
March 2009
– Vandermonde-subspace frequency division multiplexing (VFDM)
» a weighted water-filling algorithm that allows a
secondary cognitive-like system to exploit the left-
over degrees of freedom by the primary system
Orthogonal LTE two-tier Cellular Networks, IEEE ICC 2011
25
Other B4G radio technologies
• Full duplex" wireless technology (Rice University)
– A new technology that allows devices to send and receive data at the same time could debut in a few years as the foundation for future 5G networks,
5G wireless research nears breakthrough
http://www.msnbc.msn.com/id/44442743/ns/technology_and_science-wireless/#.Tvx3MUqqRNc
1426
Outline
• Generation aspects
• Drivers for 5G Wireless
– Radio aspect
– Access aspect
– Network aspect
– Service aspects
– Terminal aspect
• Conclusion
27
Access networks for 5G Wireless
• 2001
– Cellular
– WLAN
• 2005
– Ad Hoc
• 2010
– Ubiquitous access
– Access virtualization
28
Intelligent Ubiquitous Access
29
A question?• What is the last word yelled by Mel Gibson in the movie
“ Braveheart” (the 68th Academy Award for Best Picture in 1995)?
•
Cognitive Radio
• Definition: Cognitive Radio (CR) is a radio that can
change its parameters based on interaction with the
environment in which it operates.
32
Cognitive Radio Systems
CR has three ways to avoid interfering non-congnitive users, based
on the required knowledge (from small to large):
Underlaying: interference power to primary users below a prescribed threshold
Interweaving: activity of primary users to access vacant frequency spectrum
Overlaying: codebooks and/or message of primary users to apply sophisticated signal processing to control the interference
Related Publications
• Interference Mitigation in Underlaying HCR Using
Beamforming (IEEE T. Comm. 2012)
• Interference Avoidance in Interweaving HCR Using
Spectrum Handoff (IEEE T. Comm. 2012)
33
Hierarchical Cell Design
CB : Coordinated Beamforming
JP : Joint Processing
34
35
Toward Optimal Multiuser Antenna
Beamforming for Hierarchical Cognitive
Radio Systems (IEEE Trans. on Comm’12)
We consider a hierarchical CR system where an underlying
microcellular system simultaneously shares the same spectrum
resource with a macrocellular system.
Challenges to hierarchical CR systems:
manage interference between the microcell and the macrocell
protect primary users from being interfered by secondary users
maximize secondary users’ throughput
Our objective is to overcome the challenges by using multiple
antenna beamforming. (As we know, multiple antennas have
capability to increase capacity and reduce interference.)
36
Hierarchical CR System Models
0
0
: beamforming weight for the secondary user
: channel gain between secondary BS
and primary BS
: channel gain between secondary BS
and the secondary user
: channel gain betw
j
j
jth
jth
g
w
h
h
0
1
een primary user and primary BS
: channel gain between primary user
and the secondary user
: distance between primary BS and secondary BS
: distance between primary BS and secodnary user
jg
jth
D
D
0
s
: distance between primary user and primary BS
: distance between primary user
and the scodnary users
j
d
d
jth
FDD
TDD
37
Hierarchical CR System Models
SINR for the kth secondary user:
Sum rate for all the underlying secondary users:
38
Joint Multiuser Beamforming and Power
Allocation
Optimization problem:
where .
The power allocated to secondary users is implicitly defined in the
power norm of the beamforming weighting vectors.
interference power constraint
SINR constraints
total transmission power constraint
39
Simulation Setup
Geographical locations:
40
Achievable Sum RatesThe optimal deployment
for underlying CR systems:
• at most seven antennas
for concurrent
transmission
• at most a cell radius of
200m~300m for cell
coverage
Related Publications
• Interference Mitigation in Underlaying HCR Using
Beamforming (IEEE T. Comm. 2012)
• Interference Avoidance in Interweaving HCR Using
Spectrum Handoff (IEEE T. Comm. 2012)
41
42
Key Feature in Cognitive Radio Networks:
Multiple Handoffs
• A secondary user (SU)’s connection may execute
multiple handoffs during its transmission period due
to the interruptions from the primary users (PUs).
Frequency
Power
Time
PUPU
PUPU
PU
PUSU
Spectrum Handoff
Transmission time of SU’s connection
Wireless Systems Lab, NCTU, Taiwan 43
Spectrum Management Techniques for CR
• In order to overcome the issue due to spectrum handoffs, various
spectrum management techniques (spectrum sensing, decision,
sharing, and mobility) in CR networks are re-examined from a link
connection quality perspective.
Spectrum
Decision
Spectrum Sharing
Channel 1
Channel 2
Spectrum Sensing
Spectrum Mobility Spectrum Handoff
Channel M
Arrivals
of
secondary
users
Wireless Systems Lab, NCTU, Taiwan 44
Queuing-Theoretical Spectrum Management
Techniques for Cognitive Radio Networks
(IEEE Wireless Comm’11)
Spectrum
Mobility
(Part III)Spectrum
Decision
(Part II)
Spectrum
Sharing
(Part IV)
Analysis of Proactive
Spectrum Handoff
Optimal Target Channel
Sequence for Proactive
Spectrum Handoff
Analysis of Reactive
Spectrum Handoff
Load-Balancing
Spectrum Decision
Interference-Avoiding
Spectrum Sharing
Modeling Technique for
Cognitive Radio Networks
PRP M/G/1 Queueing Network Model
(Part I)
(IEEE JSAC’12, TCOM’12, TMC’12)
(IEEE JSAC,’11)
Wireless Systems Lab, NCTU, Taiwan 45
An Issue
• How to evaluate the average data delivery time for
reactive and proactive spectrum handoff taking account of
multiple interruptions from primary useres?
• Note:
– Reactive spectrum handoff: sensing-based target channel sequence
– Proactive spectrum handoff: predetermined target channel sequence
46
Multiple Handoffs and Its
Target Channel Sequences: handoff processing time for staying in the current channel
c: handoff processing time for changing to another channel,
Target channel sequence = (2,2,1)
47
Extended Data Delivery Time (T)
• Average extended data delivery time is
where N is the total number of interruptions of a
secondary connection.
48
Derivation of E[T|N=n]
Service timeMultiple handoffs delay
E[Xs()] is the service of the secondary connection whose default channel is .
E[Di] is the handoff delay for the ith interruption.
The target channels selection strategy can affect the value of the second term.
SCA
Existing PCs or SCsCh1
Ch2
Extended Data Delivery Time (T)
Existing PCs or SCs
SCA
PCs SCA
Ch3 Existing PCs or SCs SCA
Existing PCs or SCs
Existing PCs or SCs
Arrival of Primary User Spectrum Handoff
D1 D
2D
3
ts
ts
49
Analytical Model for Proactive
Spectrum Handoff
• Example: total number of channels = 3
Predetermined-based target channel selection
Wireless Systems Lab, NCTU, Taiwan 50
Problem Formulation
• Determine a target channel sequence minimize athe
average cumulative handoff delay E[D] for a newly
arriving secondary user's connection. Formally, we
have
Exhaustive search method takes O(ML) time.
required length L of the target channel sequence
Optimal Dynamic-Programming-Based Target Channel
Selection Algorithm (IEEE Trans. on COMM’12)
• We observe that the considered optimization problem has
the optimal substructure property based on the developed
state diagram.
1,0 1,1
2,1
3,1
1,2
2,2
3,2
1,3
2,3
3,3
Stage 1Stage 0 Stage 2 Stage 3
1,4
2,4
3,4
Stage 4
Target channel sequence = (1,2,3,1) Target channel sequence = (1,2,3,2)
state (k,i): channel k is selected for the target channel at the ith interruption
Dynamic-programming-based algorithm takes O(LM2).
Effects of Arrival rates on Handoff Delay
52
53
Analytical Model for Reactive Spectrum
Handoff (IEEE JSAC’12)
• Example: total number of channels = 3
sensing-based target channel selection
Wireless Systems Lab, NCTU, Taiwan 54
Performance Comparison of Various Target
Channel Selection Methods
• The sensing technology can effectively shorten the average
cumulative handoff delay only when sensing time ·2
compared to the always-staying strategy
p(1)=p
(2)= p
(s(1),s
(2))=(0.02,0.02)
(E[Xp(1)],E[Xp
(2])= (5,5)
(E[Xs(1)],E[Xs
(2])= (10,10)
Wireless Systems Lab, NCTU, Taiwan 55
Summary
• We formulate an optimization problem of finding a
target channel sequence for multiple handoffs, and a
dynamic-programming-based algorithm with time
complexity of O(LM2) is proposed. (IEEE Tran. On
Comm., 2012)
• We provide a framework to determine whether the
sensing technology can effectively shorten the
extended data delivery time under various sensing
time. (IEEE JSAC. 2012)
• Other works related to spectrum decision for load
balancing in HCR are also developed (IEEE
JSAC’11)
Outline
• Generation aspects
• Drivers for 5G Wireless
– Radio aspect
– Access aspect
– Network aspect
– Service aspects
– Terminal aspect
• Conclusion
56
Core Networks for 5G Wireless
• 2001
– UMTS
• 2005
– IP Backbone
» QoS
» Mobility
» Modular Protocols
• 2010
– Programmable Networks?
57
OpenFlow decouple control and cata Information,
thereby helping create new functions in programmable
networks
Hardware
Hardware
Hardware
Hardware
Hardware
Operation
System
Operation
System
Operation
System
作業系統
Operation
System
Network Operation Systems
Function Function
Function Function
Function Function
Function Function
功能 功能
功能 功能
Switch is dedicated to data exchange
Functions and OS are implementedIn Controller, such asNOX
58
Outline
• Generation aspects
• Drivers for 5G Wireless
– Radio aspect
– Access aspect
– Network aspect
– Service aspects
– Terminal aspect
• Conclusion
59
Service Aspect for 5G Wireless
Source: Fujitsu 60
What is human centric service?
61
JOIN: A mobile social network application
Objective: Provide immediate and personalized LBS information for a group of users.
Real-time meet-up activities for a group of mobile users
Integrating GPS, cloud computing, smart phone and wireless communications.
A on-line LBS service beyond the combination foursquares.com and latitudes.com.
62
Outline
• Generation aspects
• Drivers for 5G Wireless
– Radio aspect
– Access aspect
– Network aspect
– Service aspects
– Terminal aspect
• Conclusion
64
Terminal Aspect for 5G Wireless
• 2001
– Single-mode
• 2005
– Multi-mode
– Open architecture
• 2010
– Reconfigurability
– Cognitive Radio or more?
65
Is Smartphone Smart Enough?
• Connecting to heterogeneous wireless networks
– 4G/B4G, WiFi, WiMAX, Bluetooth, NFC
• Smart Computing: context aware and low power
− Visual: cameras
− Audio: microphone, microphone array
− Multi-touch
– Location: GPS, compass
– Motion: accelerometers, proximity sensors
– Ambient: light, thermometers, humidity, pressure
– Physiological: temperature, galvanic skin response (GSR), pulse, respiration
– Chemical: electronic nose, electronic tongue
• Output
− Visual: HD display, pico-projectors
− Audio: speakers
− Vibrator
− …
Future Multiple-Sense Mobile Device
• Human Centric Phones
– Se-So-Mo-Lo (思索摸路) Sensor + Social +Mobile + Local
67
臺灣雲端技術發展策略論壇
Mobile Augmented Reality• Wikitude: travel guide application for Android
– Point the phone’s camera at a point of interest
– GPS to superimpose distances to points of interest
– Compass to keep track of where you’re looking
– The application looks up what it sees in its online database
Some MAR Examples
• Word Lens
• AR Soccer
Sensor-Assisted Rescue Service Architecture in
Mobile Cloud Computing (IEEE WCNC’13)
• Motion-Driven Sensing Scheme
Accelerometer +Touch screen
• Voice-Driven Sensing Scheme
Volume sensors + speech recognition
音量感測器觸發語音辨識機制
+
70
+
Challenges and Opportunities for
Smartphones
71
Outline
• Generation aspects
• Drivers for 5G Wireless
– Radio aspect
– Access aspect
– Network aspect
– Service aspects
– Terminal aspect
• Conclusion
72
Evolution to 5G Wireless
2001 2005 2010
Service Network
Access Network
Radio Technology
Terminal
Technology
Core Network
2G Services
(Computer-
centric)
Cyberworld
Services (human-
centric)
Ubiquitous Services
(Network-centric)
UMTS
Programmable
Networks
IP Backbone
Cellular
WLAN
PAN
Ad Hoc
Intelligent
Ubiquitous
Access
CDMA
TDMA
Integrated
Aggregated
Multiple Access OFDMA
MIMONetwork
MIMO
Single-
mode
Multi-mode Reconfigurability Cloud-based
Cognitive
Devices
2015
MobilityOoS
Modula
Protocol
73
Clarifications
• 5G is not officially defined term.
• 4G offers theoretically closer to Gigabit Ethernet whereas
users expect multiple Gigabit speed from 5G.
• Typical 5G concept would be raised in somewhere
around 2013-2015.
• Ideal 5G model should accommodate the challenges and
accommodate the short falls of the 4G Technology and 4G
deployment experiences.
874
Conclusion
• According to the key features of IMT-Advanced, many challenges in 4G
remained are remained to be solved in 5G, especially the interworking with other
radio access technologies.
• The trend that the ICT Paradigm shift from network centric to human centric will
significantly affect the evolution of 5G wireless.
– Aggregated heterogeneous radio access
– Cloud-based cognitive devices
– Programmable/active networks
• Cooperation of multiple sensors, multiple users, multiple base stations, and
multiple systems is the key to the success of 5G wireless!
• Smartphones = gateway to the cloud, and the bridge of sensors
75
References
Network MIMO
• Li-Chun Wang and Chu-Jung Yeh, “3-Cell network MIMO architectures with sectorization
and fractional frequency reuse,” IEEE Journal in Selected Area in Communications, Vol. 29,
No. 6, pp. 1185~1199, June, 2011
• Li-Chun Wang and Chu-Jung Yeh, “Antenna architectures for network MIMO,” Cooperative
Cellular Wireless Networks,” Cambridge University Press, ISBN-13: 9780521767125.
Scheduling for Multi-User MIMO
• Li-Chun Wang and Chu-Jung Yeh, “Scheduling for Multiuser MIMO Broadcast Systems:
Transmit or Receive Beamforming?” IEEE Trans. on Wireless Communications, Vol. 9, No.
9. pp. 2779~2791, Sep. 2010.
• C. J. Chen and Li-Chun Wang, "Enhancing Coverage and Capacity for Multiuser MIMO
Systems by Utilizing Scheduling, ” IEEE Trans. on Wireless Communications, Vol. 5, No. 5,
pp. 1148-1157, May, 2006.
• C. J. Chen and Li-Chun Wang, “A Unified Capacity Analysis for Wireless Systems with Joint
Multiuser Scheduling and Antenna Diversity in Nakagami Fading Channels,” IEEE Trans. on
Communications, vol. 54, No. 3, pp. 469~478, Mar. 2006.
• IEEE C802.16m-09/2280 “Frequency Planning for Inter-Cell Interference Reduction in 3-Cell Collaborative MIMO Systems”
76
References
Small Cells
• Tutorial at IEEE VTC’13, Spring, Dresdon, Gemany: Small Cells
Technologies in LTE-Advanced and Beyond
•Meng-Lin Ku, Li-Chun Wang, and Yu-Ted Su, "Toward Optimal Multiuser
Antenna Beamforming for Hierarchical Cognitive Radio Systems,” IEEE Trans.
on Communications, Vol.60, No. 10, pp. 2872 ~2885, Oct. 2012.
•Meng-Lin Ku, Qingchun Chen, Saeed S. Ghassemzade, Vahid Tarokh and Li-Chun
Wang, “Service Coverage for Cognitive Radio Networks with Cooperative Relays in
Shadowed Hotspot Areas,” IEEE WCNC 2012
•Ang-Hsun Tsai, Li-Chun Wang, Jane-Hwa Huang, Ruey-Bing Hwang, “High-
Capacity OFDMA Femtocells by Directional Antennas and Location Awareness,”
IEEE Systems Journal, Vol. 6, No. 2, pp. 329-340, June 2012
•Ang-Hsun Tsai, Li-Chun Wang and Jane-Hwa Huang, "Overload Control for
Machine Type Communications with Femtocells" IEEE VTC Fall, Sep., 2012.
•Yu-Jung Liu, Meng-Lin Ku and Li-Chun Wang, "Joint Beamforming, Scheduling,
and Power Allocation for Hierarchical Cellular Systems,” accepted by International
Conference on Communications (ICC), 2012.
•Ang-Hsun Tsai, Li-Chun Wang, Jane-Hwa Huang, Ruey-Bing Hwang, “Stable
Subchannel Allocation for OFDMA Femtocells with Switched Multi-beam Directional
Antennas,” IEEE Globecom, Dec., 2011. 77
References
Cognitive Radio
•Chung-Wei Wang and Li-Chun Wang, “Analysis of Reactive Spectrum Handoff in Cognitive
Radio Networks,” IEEE Journal in Selected Areas in Communications in Nov., Vol. 30, No. 10,
pp. 2016~2018, Nov. 2012.
•L. C. Wang, C. W. Wang, and C. J. Chang, "Optimal Target Channel Sequence Design for
Multiple Spectrum Handoffs in Cognitive Radio Networks," IEEE Transactions on
Communications, Vol. 11, No. 9, pp. 1499-1513, Sep., 2012.
•Li-Chun Wang, Chung-Wei Wang, and Chung-Ju Chang, “Modeling and Analysis for Spectrum
Handoffs in Cognitive Radio Networks,” IEEE Trans. on Mobile Computing, Vol. 11, No. 9, pp.
1499-1533, Sep., 2012
•Ching-Chun Huang and Li-Chun Wang, “Dynamic Sampling Rate Adjustment for Compressive
Spectrum Sensing over Cognitive Radio Network,” IEEE Wireless Communications Letter, Vol.
1, No. 2, pp. 57-60, Apr. 2012.
•Li-Chun Wang, Chung-Wei Wang, and Kai-Tien Feng, “A Queueing-Theoretical Framework for
QoS-Enhanced Spectrum Management in Cognitive Radio Networks,” IEEE Wireless
Communications Magazine, pp. 18-26, Dec., 2011.
• Li-Chun Wang, Chung-Wei Wang, and Fumiyuki Adachi, “Load Balancing Spectrum
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78
References
Cloud Networking
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Disjoint ENDIST-based Layer-2 Routing Algorithm for Cloud Datacenters” IEEE
Globecom, Dec., 2012.
•Wei-Ping Yang, Li-Chun Wang, Hung-Pin Wen, “A Queueing Analytical Model for
Service Mashup in Mobile Cloud Computing,” IEEE Wireless Communications
Networking Conference, Apr. 2013.
Cloud Services and Smartphones
•Chia-Yu Lin, Yu-Jia Chen, Li-Chun Wang and Yu-Chee Tseng, "A No-Touch Mechanism to
Initiate Mobile Applications on Smart Phones" IEEE VTC Fall, Sep., 2012.
•Yu-Jia Chen and Li-Chun Wang, “A Security Framework of Group Location-Based Mobile
Applications in Cloud Computing,” International Workshop on Security in Cloud Computing, Sep.
2011
•Yu-Jia Chen, Chia-Yu Lin, Li-Chun Wang, “Sensor-Assisted Rescue Service Architecture in
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79
References
Cloud Services
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Computing Technology and Science (CloudCom), 2012.
• C. Y. Lin, Y. J. Chen, L. C. Wang and Y. C. Tseng, " An Efficient Meet-Up Mechanism by Mashing-
up Social and Mobile Clouds, " Microsoft Research Cloud Futures Workshop 2012 (Cloud Futures
), 2012.
• Y. J. Chen and L. C. Wang, "A Security Framework of Group Location-Based Mobile Applications in
Cloud Computing, "IEEE International Conference on Parallel Processing Workshops (ICPPW),
2012
Multi-sensed Smartphones
• Y. J. Chen, C. Y. Lin, and L. C. Wang, “Sensors-Assisted Rescue Service Architecture in Mobile
Cloud Computing,” IEEE Wireless Communications Networking Conference (WCNC), 2013.
• C. Y. Lin, Y. J. Chen, L. C. Wang and Y. C. Tseng, " A Proximity Sensor Based No-Touch
Mechanism for Mobile Applications on Smart Phones, " IEEE Vehicular Technology Conference,
September 2012.
• C. Y. Lin, Y. J. Chen, L. C. Wang and Y. C. Tseng, "Proximity-Based Speech Recognition in Mobile
Cloud Computing, " 2nd International Workshop on Mobile Sensing (IPSN Workshop) , 2012.
• I. C. Shen、Y. J. Chen、C. Y. Lin and L. C. Wang, " An Experience of Teaching Mobile Cloud
Computing, " IEEE Technology and Engineering Education (ITEE), Vol 7, No 2 (June, 2012)
80
Final remarks
Coming together is a beginning.
Keeping together is progress.
Working together is success.
~ Henry Ford
ありがとうございました
81
1
Backup
83
Solving Optimization Problem
It is nontrivial to solve the joint optimization problem because the
objective function of the optimization problem is apparently non-
concave.
We introduce auxiliary variables in the objective function to limit the
intra-user interference power among the secondary users:
auxiliary variables
84
Solving Optimization Problem
Without loss of generality, we further restrict that the matched output
between the beamforming weight and channel response is merely a
non-negative real value:
Convex
Optimization
problem
85
Iterative Rate Maximization (IRM)
AlgorithmIn conclusion, we can increase the sum rate by reducing
the values of Ω until the intra-user interference power
constraint is active, while we should make a tradeoff
between the intra-user interference power and the
matched output power in the active case to further
improve the sum rate.
Y
Initialization 0
Set a feasible
and , 1i k
Ω
0
1Find R Ω
Update
1 1
C hoose 0< 1
1i i i
k k
Ω Ω e
Convex Opt.
1Find
i
R Ω
ˆand i
W
k K
N
Y
Stop
N
Set 1i i Set 1
and 1
i i
k k
1
1 1
i i
TR R
Ω Ω
1
1
1 1
1
Set
ˆ ˆ
i i
i i
i i
R R
Ω Ω
Ω Ω
W W
