【資料名稱】：Game Theory in Wireless and Communication Networks Theory, M

【資料作者】：ZHU HAN,DUSIT NIYATO,WALID SAAD,

【資料日期】：Cambridge University Press 2012

【資料語(yǔ)言】：英文

【資料格式】：PDF

【資料目錄和簡(jiǎn)介】：

This uni􀅿ed treatment of game theory focuses on 􀅿nding state-of-the-art solutions to
issues surrounding the next generation of wireless and communication networks. Future
networks will rely on autonomous and distributed architectures to improve the ef􀅿ciency
and 􀆀exibility of mobile applications, and game theory provides the ideal framework
for designing ef􀅿cient and robust distributed algorithms. This book enables readers to
develop a solid understanding of game theory, its applications, and its use as an effective
tool for addressing various problems in wireless communication and networking.
The key results and tools of game theory are covered, as are various real-world
technologies including 3G/4G networks, wireless LANs, sensor networks, cognitive
networks, and Internet networks. The book also covers a wide range of techniques
for modeling, designing, and analyzing communication networks using game theory,
as well as state-of-the-art distributed design techniques. This is an ideal resource for
communications engineers, researchers, and graduate and undergraduate students.

Preface page xv
1 Introduction 1
1.1 Brief introduction to the history of game theory 1
1.2 Game theory in wireless and communication networks 3
1.3 Organization and targeted audience 4
1.3.1 Timeliness of the book 6
1.3.2 Outline of the book 9
2 Wireless networks: an introduction 14
2.1 Wireless channel models 15
2.1.1 Radio propagation 15
2.1.2 Interference channel 20
2.2 Categorization of wireless networks 21
2.2.1 3G cellular networks and beyond 21
2.2.2 WiMAX networks 25
2.2.3 WiFi networks 27
2.2.4 Wireless personal area networks 31
2.2.5 Wireless ad hoc networks 37
2.2.6 Wireless sensor networks 40
2.3 Advanced wireless technology 45
2.3.1 OFDM technology 45
2.3.2 Multiple-antenna systems 47
2.3.3 Cognitive radio 49
Part I Fundamentals of game theory
3 Non-cooperative games 55
3.1 Non-cooperative games: preliminaries 55
3.1.1 Introduction 55
3.1.2 Basics of non-cooperative games 56
viii Contents
3.2 Non-cooperative games in strategic form 58
3.2.1 Matrix games 58
3.2.2 Dominating strategies 61
3.2.3 Nash equilibrium 63
3.2.4 Static continuous-kernel games 65
3.2.5 Mixed strategies 69
3.2.6 Ef􀅿ciency and equilibrium selection 72
3.3 Dynamic non-cooperative games 74
3.3.1 Non-cooperative games in extensive form 74
3.3.2 Repeated games 80
3.3.3 Stochastic games 84
3.4 Special classes of non-cooperative games 85
3.4.1 Potential games 85
3.4.2 Stackelberg games 88
3.4.3 Correlated equilibrium 91
3.4.4 Supermodular games 94
3.4.5 Wardrop equilibrium 96
3.5 Summary 100
4 Bayesian games 101
4.1 Overview of Bayesian games 101
4.1.1 Simple example 101
4.1.2 Static Bayesian game 102
4.1.3 Bayesian dynamic games in extensive form 104
4.1.4 Cournot duopoly model with incomplete information 105
4.1.5 Auction with incomplete information 107
4.2 Applications in wireless communications and networking 109
4.2.1 Packet-forwarding game 109
4.2.2 K-player Bayesian water-􀅿lling game 112
4.2.3 Channel-access game 116
4.2.4 Bandwidth-auction game 119
4.2.5 Bandwidth-allocation game 121
4.3 Summary 122
5 Differential games 124
5.1 Optimal-control theory 125
5.1.1 Dynamic programming 125
5.1.2 The maximum principle 126
5.2 Differential games 128
5.2.1 Main ingredients and general results 128
5.2.2 Stackelberg differential game 130
5.3 Applications of differential games in wireless communications
and networking 136
5.4 Summary 137
Contents ix
6 Evolutionary games 138
6.1 The evolutionary process 139
6.1.1 Evolutionarily stable strategies 139
6.1.2 Replicator dynamics 141
6.1.3 The evolutionary game and reinforcement learning 143
6.2 Applications of evolutionary games in wireless communications and
networking 144
6.2.1 Congestion control 144
6.2.2 Evolutionary game for the Aloha protocol 146
6.2.3 Evolutionary game for WCDMAaccess 148
6.2.4 Routing-potential game 149
6.2.5 Cooperative sensing in cognitive radio 151
6.2.6 TCP throughput adaptation 154
6.2.7 User churning behavior 158
6.2.8 Dynamic bandwidth allocation with evolutionary network
selection 163
6.3 Summary 170
7 Cooperative games 171
7.1 Bargaining theory 171
7.1.1 Introduction 171
7.1.2 The Nash bargaining solution 172
7.1.3 Sample applications in wireless and communication networks 178
7.2 Coalitional game theory: basics 185
7.2.1 Introduction 185
7.2.2 Coalitional-game theory: preliminaries 185
7.3 Class I: canonical coalitional games 189
7.3.1 Main properties of canonical coalitional games 189
7.3.2 The core as a solution for canonical coalitional games 190
7.3.3 The Shapley value 195
7.3.4 The nucleolus 196
7.3.5 Sample applications in wireless and communication networks 198
7.4 Class II: coalition-formation games 203
7.4.1 Main properties of coalition-formation games 203
7.4.2 Impact of a coalitional structure on solution concepts for
canonical coalitional games 203
7.4.3 Dynamic coalition-formation algorithms 205
7.4.4 Sample applications in wireless and communication networks 209
7.5 Class III: coalitional graph games 215
7.5.1 Main properties of coalitional graph games 215
7.5.2 Coalitional graph games and network-formation games 216
7.5.3 Sample applications in wireless and communication networks 219
7.6 Summary 220
x Contents
8 Auction theory and mechanism design 221
8.1 Introduction and auction basics 222
8.2 Mechanism design 226
8.2.1 Equilibrium concepts 226
8.2.2 Participation and incentive compatibility 227
8.2.3 Revelation principle 228
8.2.4 Budget balance and ef􀅿ciency 228
8.2.5 Groves mechanism 229
8.2.6 Impossibility and possibility 229
8.3 Special auctions 230
8.3.1 VCG auction 230
8.3.2 Share auction 232
8.3.3 Double auction 233
8.4 Examples of communication applications 235
8.4.1 Cognitive radio 236
8.4.2 Physical-layer security 248
8.5 Summary 251
Part II Applications of game theory in communications and networking
9 Cellular and broadband wireless access networks 255
9.1 Uplink power control in CDMA networks 257
9.1.1 Single-cell CDMA networks 258
9.1.2 Multi-cell wireless CDMA networks 263
9.2 Resource allocation in single-cell OFDMA networks 269
9.2.1 OFDMA resource-allocation model 270
9.2.2 Nash bargaining solution for subcarrier allocation 272
9.2.3 Algorithms for reaching the Nash bargaining solution 274
9.3 Power allocation in femtocell networks 279
9.3.1 Femtocell power control as a Stackelberg game 280
9.3.2 Multi-leader multi-follower Stackelberg equilibrium 284
9.3.3 Algorithm for reaching the Stackelberg equilibrium 286
9.4 IEEE 802.16 broadband wireless access networks 287
9.4.1 Resource allocation and admission control 287
9.4.2 Relay-station deployment in IEEE 802.16j 299
9.5 Network selection in multi-technology wireless networks 307
9.5.1 Network selection as a non-cooperative game 309
9.5.2 Network selection with incomplete information 311
9.6 Summary 320
10 Wireless local area networks 321
10.1 MAC protocol design 322
10.1.1 Static game 323
Contents xi
10.1.2 Dynamic game 324
10.1.3 Deviation detection and penalization 325
10.1.4 Related work 326
10.2 Random-access control 326
10.2.1 Choice of utility function 327
10.2.2 Dynamics of a random-access game 328
10.2.3 Extension with propagation delay and
estimation error 329
10.2.4 Related work 329
10.3 Rate selection for VoIP service on WLAN 330
10.3.1 Game formulation 330
10.3.2 Payoff function 331
10.4 Access-point selection 332
10.4.1 Formulation of a population game 333
10.4.2 Price of anarchy 335
10.4.3 Access pricing 335
10.4.4 Related work 336
10.5 Admission control 337
10.5.1 Two-player game formulation 337
10.5.2 Interpretation of payoff 339
10.6 WiFi access-point pricing 339
10.6.1 Pricing scheme for direct payment 340
10.6.2 User withWeb browsing 341
10.6.3 User with 􀅿le transfer 342
10.6.4 Model for uncertain application 343
10.7 Summary 344
11 Multi-hop networks 345
11.1 Routing-game basics 345
11.2 Cooperation enforcement and learning using a repeated game 349
11.2.1 System model and problem formulation 349
11.2.2 Self-learning cooperation-enforcing framework 350
11.2.3 Asynchronous network 352
11.2.4 Case analysis and performance evaluations 353
11.3 Hierarchical routing using a network-formation game 357
11.3.1 System model and game formulation 358
11.3.2 Hierarchical network-formation game solution 362
11.3.3 Hierarchical network-formation algorithm 364
11.3.4 Simulation results and analysis 366
11.4 Other typical approaches 369
11.4.1 Price-based solution 369
11.4.2 Truthfulness and security using auction theory 370
11.4.3 Evolutionary-game approach 372
11.5 Summary 373
xii Contents
12 Cooperative-transmission networks 375
12.1 Basics of cooperative transmission 376
12.1.1 Cooperative-transmission protocols 376
12.1.2 State of the art and impact on different layers 380
12.2 Non-cooperative game for relay selection and power control 380
12.2.1 Relay-selection and power-control problem 381
12.2.2 Stackelberg-game approach 382
12.3 Auction-theory-based resource allocation 389
12.3.1 Resource-allocation objectives 389
12.3.2 Share-auction approach 392
12.4 Cooperative transmission using a cooperative game in MANET 399
12.4.1 Sel􀅿shness in packet-forwarding networks 400
12.4.2 Cooperative transmission using a coalitional game 402
12.5 Cooperative routing 411
12.5.1 Cooperative-routing algorithms 412
12.5.2 WiMAX IEEE 802.16j 413
12.6 Summary 416
13 Cognitive-radio networks 418
13.1 Cooperative spectrum sensing 421
13.1.1 System model 421
13.1.2 Coalitional-game formulation 423
13.1.3 Centralized approach and performance comparison 426
13.2 Power allocation as a non-cooperative game 426
13.2.1 Underlay spectrum access and power allocation 426
13.2.2 Properties of the Nash equilibrium for power allocation 428
13.2.3 Distributed algorithm 429
13.2.4 Pigouvian taxation and social optimality 431
13.2.5 Related work 432
13.3 Medium access control 432
13.3.1 Channel allocation 433
13.3.2 Channel access 434
13.3.3 Distributed algorithms 435
13.4 Decentralized dynamic spectrum access 436
13.4.1 Overlay dynamic spectrum access 436
13.4.2 Utility function 438
13.4.3 Decentralized algorithm for channel access 439
13.4.4 Alternative algorithms 440
13.5 Radio resource competition based on a stochastic learning game 441
13.5.1 System model of radio resource competition 441
13.5.2 Auction mechanism 442
13.5.3 Secondary-user strategy 443
13.5.4 Learning algorithm 445
Contents xiii
13.6 Cheat-proof strategies for open spectrum sharing 446
13.6.1 One-shot non-cooperative game 446
13.6.2 Cooperative strategy 447
13.6.3 Repeated games 448
13.6.4 Cheat-proof strategy 449
13.7 Spectrum leasing and cooperation 450
13.7.1 Game formulation with instantaneous CSI 451
13.7.2 Game formulation with long-term CSI 454
13.8 Service-provider competition for dynamic spectrum allocation 455
13.8.1 User demand 455
13.8.2 Optimal price 457
13.8.3 Related work 458
13.9 Summary 458
14 Internet networks 460
14.1 Combined 􀆀ow control and routing in communication networks 462
14.1.1 Single user with multiple links 463
14.1.2 Multiple users with multiple parallel links 465
14.1.3 Sample Nash equilibria 471
14.2 Congestion control in networks with a single service provider 473
14.2.1 Pricing and congestion control 474
14.2.2 Non-cooperative Nash game between followers 476
14.2.3 Optimal pricing policy for the service provider 478
14.2.4 Network with a large number of followers 479
14.3 Pricing and revenue sharing for Internet service providers 481
14.3.1 Pricing game among Internet service providers 482
14.3.2 Revenue-sharing strategies 484
14.3.3 Distributed algorithm for 􀅿nding a Nash equilibrium 485
14.4 Cooperative 􀅿le sharing in peer-to-peer networks 487
14.4.1 Cooperative vs. non-cooperative 􀅿le sharing 489
14.4.2 File sharing as a coalitional game in partition form 491
14.4.3 Distributed algorithm for coalition formation 493
14.4.4 Coalition formation in two-peer and N-peer networks 495
14.5 Summary 499
References 501
Index 530