An Optimization Theoretical Framework for Resource Allocation over Wireless Communications

321 Pages·2003·2.04 MB·English

ABSTRACT
Title of Dissertation: An Optimization Theoretical Framework for
Resource Allocation over Wireless Networks
Zhu Han, Doctor of Philosophy, 2003
Dissertation directed by: Professor K. J. Ray Liu
Department of Electrical and Computer Engineering
With the advancement of wireless technologies, wireless networking has become
ubiquitous owing to the great demand of pervasive mobile applications. Some
fundamental challenges exist for the next generation wireless network design such
as time varying nature of wireless channels, co-channel interferences, provisioning
of heterogeneous type of services, etc. So how to overcome these difficulties and
improve the system performance have become an important research topic.
Dynamic resource allocation is a general strategy to control the interferences
and enhance the performance of wireless networks. The basic idea behind dynamic
resourceallocationistoutilizethechannelmoreefficientlybysharingthespectrum
and reducing interference through optimizing parameters such as the transmitting
power, symbol transmission rate, modulation scheme, coding scheme, bandwidth,
etc. Moreover, the network performance can be further improved by introducing
diversity, such as multiuser, time, frequency, and space diversity. In addition, cross
layerapproachforresourceallocationcanprovideadvantagessuchaslowoverhead,
more efficiency, and direct end-to-end QoS provision.
The designers for next generation wireless networks face the common problem
of how to optimize the system objective under the user Quality of Service (QoS)
constraint. There is a need of unified but general optimization framework for
resource allocation to allow taking into account a diverse set of objective functions
with various QoS requirements, while considering all kinds of diversity and cross
layer approach. We propose an optimization theoretical framework for resource
allocation and apply these ideas to different network situations such as:
• Centralized resource allocation with fairness constraint
• Distributed resource allocation using game theory
• OFDMA resource allocation
• Cross layer approach
On the whole, we develop a universal view of the whole wireless networks from
multiple dimensions: time, frequency, space, user, and layers. We develop some
schemes to fully utilize the resources. The success of the proposed research will
significantly improve the way how to design and analyze resource allocation over
wireless networks. In addition, the cross-layer optimization nature of the problem
provides an innovative insight into vertical integration of wireless networks.
An Optimization Theoretical Framework for
Resource Allocation over Wireless Networks
by
Zhu Han
Dissertation submitted to the Faculty of the Graduate School of the
University of Maryland, College Park in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
2003
Advisory Committee:
Professor K. J. Ray Liu, Chairman/Advisor
Professor Steve Tretter
Professor Sennur Ulukus
Professor Gang Qu
Professor Carlos A. Berenstein
(cid:176)c Copyright by
Zhu Han
2003
DEDICATION
To my wife as well as my parents and sister in China.
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ACKNOWLEDGEMENTS
First of all, I would like to express my gratitude to my advisor, professor K.J.
Ray Liu. When I was bewildered in research a few years ago, he helped me find
a good direction and placed his confidence in me like a light house in the foggy
bay. I wish to thank him for putting so many efforts to help me conduct high
quality works especially in how to present. I also value his influences for my
vision, attitude, energy, and desire of my professional and personal developments.
I am grateful to my parents for their love and unconditional support. They
have made countless efforts to let me have better education chances and make sure
that I can explore my future according to my dreams. As a consequence, all my
accomplishments are also their accomplishments.
It is said that to do researches and to write a dissertation are similar to draw
a picture. The author has to dig into the topic, understand the physical meaning
and use mathematical way to describe the findings. During my 23 years of school,
I would like to explicitly thank the following people who help me develop the view
and enhance my ability of mathematics.
I would like to thank professor Cao, Zhigang and Ma, Zhengxin for their kindly
guidance during my undergraduate study in Tsinghua University. I would like to
thank professor John Baras for giving me the chance to USA for better education.
I would like to thank all the colleagues in ACTERNA (former TTC) for giving me
the happy 2.5 years for my first job. I would like to thank Dr. Jane Wang, Dr.
Farrokh Rahsid-Farrokhi, Zhu Ji, Andres Kwasinski, Guan-Ming Su, and Masoud
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Olfat for cooperations. I would like to thank professor Wu, professor Ulukus,
professor Tretter, professor Tits, professor La, and professor Mark for invaluable
suggestions on my research. I would also like to thank all the members of CSPL
lab and other friends throughout University of Maryland for giving me such happy
6.5 years.
Finally, no happiness of achievement can be completed without sharing with
my beloved one, my wife Li Wang, and with her, I am always at the top of the
world.
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TABLE OF CONTENTS
List of Tables vii
List of Figures viii
1 Introduction and Background 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.1 Wireless Channel Model . . . . . . . . . . . . . . . . . . . . 5
1.2.2 Optimal Transceiver Design . . . . . . . . . . . . . . . . . . 8
1.2.3 Multiple Access . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.2.4 Cellular Concept . . . . . . . . . . . . . . . . . . . . . . . . 15
1.2.5 Cross Layer Approaches . . . . . . . . . . . . . . . . . . . . 17
1.3 Motivations and Contributions . . . . . . . . . . . . . . . . . . . . . 19
1.4 Organization of This Dissertation . . . . . . . . . . . . . . . . . . . 22
2 Generalized Optimization Framework and Mathematics Theoret-
ical Background for Solutions 26
2.1 General Resource Allocation Formulations . . . . . . . . . . . . . . 26
2.2 Analysis Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.3 Optimal Control Solution . . . . . . . . . . . . . . . . . . . . . . . 32
2.4 Game Theory Solution . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.5 Dynamic Programming Solution . . . . . . . . . . . . . . . . . . . . 35
2.6 Comparison of Different Solutions . . . . . . . . . . . . . . . . . . . 36
3 Centralized Resource Allocation with Time Average Fairness 38
3.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2 Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.3 Joint Power Control and Adaptive Modulation . . . . . . . . . . . . 42
3.4 Link Quality and Power Management with Space-Time Diversity . . 71
3.5 Credit System, User Autonomy, and Resource Awareness . . . . . . 98
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4 Distributed Resource Allocation Using Game Theory 114
4.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.2 Basics of Game Theory . . . . . . . . . . . . . . . . . . . . . . . . . 118
4.3 System Model and Problem Formulation . . . . . . . . . . . . . . . 120
4.4 Two Level Non-cooperative Approach . . . . . . . . . . . . . . . . . 123
4.5 Comparison of Centralized and Distributed Resource Allocation . . 144
5 Channel Assignment, Throughput Allocation, and Power Control
for OFDMA 147
5.1 Introduction for OFDMA Networks . . . . . . . . . . . . . . . . . . 148
5.2 Cooperative Game Approach . . . . . . . . . . . . . . . . . . . . . . 150
5.3 Non-cooperative Game Approach . . . . . . . . . . . . . . . . . . . 169
5.4 Subspace Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
6 Cross Layer Approaches for Multiuser Communications 204
6.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
6.2 Multimedia Transmission over Wireless Networks . . . . . . . . . . 207
6.2.1 Joint Source Channel Coding . . . . . . . . . . . . . . . . . 207
6.2.2 Multiuser Cross Layer Approach . . . . . . . . . . . . . . . . 210
6.3 Joint Power Control and Blind Beamforming . . . . . . . . . . . . . 236
6.3.1 Basics for Blind Methods . . . . . . . . . . . . . . . . . . . . 236
6.3.2 Distributed Joint Scheme . . . . . . . . . . . . . . . . . . . . 239
7 Conclusions and Future Work 266
7.1 Summery and Conclusions . . . . . . . . . . . . . . . . . . . . . . . 267
7.2 Effective Bandwidth and Capacity . . . . . . . . . . . . . . . . . . . 273
7.3 Video Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . 276
7.4 Dynamic Programming over HMM . . . . . . . . . . . . . . . . . . 279
7.5 Dynamic Reinforcement Learning for Multiuser Cooperation . . . . 282
7.6 Repeated Game Approach . . . . . . . . . . . . . . . . . . . . . . . 286
7.7 Utility and Pricing for Multimedia Transmission . . . . . . . . . . . 289
7.8 Ad Hoc Networks with Limited Resources . . . . . . . . . . . . . . 293
Bibliography 297
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LIST OF TABLES
2.1 Pro and Con of Different Approaches . . . . . . . . . . . . . . . . . 37
3.1 Normalized Transmitted Power with Respect to No. of Antennas . . 68
3.2 Adaptive Algorithm for Moving Acceptable SINR Range . . . . . . . 80
3.3 Adaptive Algorithm for Uplink . . . . . . . . . . . . . . . . . . . . . 84
3.4 Adaptive Algorithm for Downlink . . . . . . . . . . . . . . . . . . . 87
3.5 Joint Beamforming and Proposed Resource Allocation . . . . . . . . 90
4.1 User Level Power Control Adaptive Algorithm . . . . . . . . . . . . 126
4.2 Distributed System Algorithm . . . . . . . . . . . . . . . . . . . . . 134
4.3 Centralized System Algorithm . . . . . . . . . . . . . . . . . . . . . 136
4.4 Strategic Form for Two Users NCTG Example . . . . . . . . . . . . 137
5.1 Two-user Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 160
5.2 Multiuser Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 164
5.3 Distributed Resource Allocation Algorithm . . . . . . . . . . . . . . 182
5.4 Initialization Algorithm A . . . . . . . . . . . . . . . . . . . . . . . 192
5.5 Initialization Algorithm B . . . . . . . . . . . . . . . . . . . . . . . 195
5.6 Iterative Capacity Improvement Algorithm . . . . . . . . . . . . . . 199
6.1 Pizza Party Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 222
6.2 Barrier Method for Performance Bound . . . . . . . . . . . . . . . . 226
6.3 Monte Carlo Method for Dynamic System . . . . . . . . . . . . . . 230
6.4 ILSP Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249
6.5 Joint Power Control and Blind Beamforming Algorithm . . . . . . . 252
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