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Game Theory in Wireless and Communication Networks: Theory, Models, and Applications [Hardback]

4.33/5 (6 ratings by Goodreads)
(University of Houston), (University of Illinois, Urbana-Champaign), , (Nanyang Technological University, Singapore), (Universitetet i Oslo)
  • Formāts: Hardback, 554 pages, height x width x depth: 244x173x38 mm, weight: 1080 g, Worked examples or Exercises; 29 Tables, black and white; 107 Line drawings, unspecified
  • Izdošanas datums: 20-Oct-2011
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 0521196965
  • ISBN-13: 9780521196963
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Game Theory in Wireless and Communication Networks: Theory, Models, and ApplicationsPalielināt
  • Formāts: Hardback, 554 pages, height x width x depth: 244x173x38 mm, weight: 1080 g, Worked examples or Exercises; 29 Tables, black and white; 107 Line drawings, unspecified
  • Izdošanas datums: 20-Oct-2011
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 0521196965
  • ISBN-13: 9780521196963
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780521196963.html
Keywords:
"This unified treatment of game theory focuses on finding state-of-the-art solutions to issues surrounding the next generation of wireless and communications networks. Future networks will rely on autonomous and distributed architectures to improve the efficiency and flexibility of mobile applications, and game theory provides the ideal framework for designing efficient 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 wireless communication and networking problems. The key results and tools of game theory are covered, as are various real-world technologies including 3G networks, wireless LANs, sensor networks, dynamic spectrum access and cognitive networks. The book also covers a wide range of techniques for modeling, designing and analysing 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"--

Provided by publisher.

Recenzijas

' a homogeneous collection of contributions in the field logically fluid, without an overwhelming load of formalism and axiomatic approaches that are, too often, offered by books on game theory aimed at engineers. We are convinced that the work will become a reference book for graduate students and network engineers interested in the design of future communication network protocols.' IEEE Communications Magazine

Papildus informācija

A unified 2001 treatment of game theory, focusing on finding state-of-the-art solutions to issues surrounding next-generation wireless and communications networks.
Preface xv
1 Introduction
1(13)
1.1 Brief introduction to the history of game theory
1(2)
1.2 Game theory in wireless and communication networks
3(1)
1.3 Organization and targeted audience
4(10)
1.3.1 Timeliness of the book
6(3)
1.3.2 Outline of the book
9(5)
2 Wireless networks: an introduction
14(41)
2.1 Wireless channel models
15(6)
2.1.1 Radio propagation
15(5)
2.1.2 Interference channel
20(1)
2.2 Categorization of wireless networks
21(24)
2.2.1 3G cellular networks and beyond
21(4)
2.2.2 WiMAX networks
25(2)
2.2.3 WiFi networks
27(4)
2.2.4 Wireless personal area networks
31(6)
2.2.5 Wireless ad hoc networks
37(3)
2.2.6 Wireless sensor networks
40(5)
2.3 Advanced wireless technology
45(10)
2.3.1 OFDM technology
45(2)
2.3.2 Multiple-antenna systems
47(2)
2.3.3 Cognitive radio
49(6)
Part I Fundamentals of game theory
3 Non-cooperative games
55(46)
3.1 Non-cooperative games: preliminaries
55(3)
3.1.1 Introduction
55(1)
3.1.2 Basics of non-cooperative games
56(2)
3.2 Non-cooperative games in strategic form
58(16)
3.2.1 Matrix games
58(3)
3.2.2 Dominating strategies
61(2)
3.2.3 Nash equilibrium
63(2)
3.2.4 Static continuous-kernel games
65(4)
3.2.5 Mixed strategies
69(3)
3.2.6 Efficiency and equilibrium selection
72(2)
3.3 Dynamic non-cooperative games
74(11)
3.3.1 Non-cooperative games in extensive form
74(6)
3.3.2 Repeated games
80(4)
3.3.3 Stochastic games
84(1)
3.4 Special classes of non-cooperative games
85(15)
3.4.1 Potential games
85(3)
3.4.2 Stackelberg games
88(3)
3.4.3 Correlated equilibrium
91(3)
3.4.4 Supermodular games
94(2)
3.4.5 Wardrop equilibrium
96(4)
3.5 Summary
100(1)
4 Bayesian games
101(23)
4.1 Overview of Bayesian games
101(8)
4.1.1 Simple example
101(1)
4.1.2 Static Bayesian game
102(2)
4.1.3 Bayesian dynamic games in extensive form
104(1)
4.1.4 Cournot duopoly model with incomplete information
105(2)
4.1.5 Auction with incomplete information
107(2)
4.2 Applications in wireless communications and networking
109(13)
4.2.1 Packet-forwarding game
109(3)
4.2.2 K-player Bayesian water-filling game
112(4)
4.2.3 Channel-access game
116(3)
4.2.4 Bandwidth-auction game
119(2)
4.2.5 Bandwidth-allocation game
121(1)
4.3 Summary
122(2)
5 Differential games
124(14)
5.1 Optimal-control theory
125(3)
5.1.1 Dynamic programming
125(1)
5.1.2 The maximum principle
126(2)
5.2 Differential games
128(8)
5.2.1 Main ingredients and general results
128(2)
5.2.2 Stackelberg differential game
130(6)
5.3 Applications of differential games in wireless communications and networking
136(1)
5.4 Summary
137(1)
6 Evolutionary games
138(33)
6.1 The evolutionary process
139(5)
6.1.1 Evolutionary stable strategies
139(2)
6.1.2 Replicator dynamics
141(2)
6.1.3 The evolutionary game and reinforcement learning
143(1)
6.2 Applications of evolutionary games in wireless communications and networking
144(26)
6.2.1 Congestion control
144(2)
6.2.2 Evolutionary game for the Aloha protocol
146(2)
6.2.3 Evolutionary game for WCDMA access
148(1)
6.2.4 Routing-potential game
149(2)
6.2.5 Cooperative sensing in cognitive radio
151(3)
6.2.6 TCP throughput adaptation
154(4)
6.2.7 User churning behavior
158(5)
6.2.8 Dynamic bandwidth allocation with evolutionary network selection
163(7)
6.3 Summary
170(1)
7 Cooperative games
171(50)
7.1 Bargaining theory
171(14)
7.1.1 Introduction
171(1)
7.1.2 The Nash bargaining solution
172(6)
7.1.3 Sample applications in wireless and communication networks
178(7)
7.2 Coalitional game theory: basics
185(4)
7.2.1 Introduction
185(1)
7.2.2 Coalitional-game theory: preliminaries
185(4)
7.3 Class I: canonical coalitional games
189(14)
7.3.1 Main properties of canonical coalitional games
189(1)
7.3.2 The core as a solution for canonical coalitional games
190(5)
7.3.3 The Shapley value
195(1)
7.3.4 The nucleolus
196(2)
7.3.5 Sample applications in wireless and communication networks
198(5)
7.4 Class II: coalition-formation games
203(12)
7.4.1 Main properties of coalition-formation games
203(1)
7.4.2 Impact of a coalitional structure on solution concepts for canonical coalitional games
203(2)
7.4.3 Dynamic coalition-formation algorithms
205(4)
7.4.4 Sample applications in wireless and communication networks
209(6)
7.5 Class III: coalitional graph games
215(5)
7.5.1 Main properties of coalitional graph games
215(1)
7.5.2 Coalitional graph games and network-formation games
216(3)
7.5.3 Sample applications in wireless and communication networks
219(1)
7.6 Summary
220(1)
8 Auction theory and mechanism design
221(34)
8.1 Introduction and auction basics
222(4)
8.2 Mechanism design
226(4)
8.2.1 Equilibrium concepts
226(1)
8.2.2 Participation and incentive compatibility
227(1)
8.2.3 Revelation principle
228(1)
8.2.4 Budget balance and efficiency
228(1)
8.2.5 Groves mechanism
229(1)
8.2.6 Impossibility and possibility
229(1)
8.3 Special auctions
230(5)
8.3.1 VCG auction
230(2)
8.3.2 Share auction
232(1)
8.3.3 Double auction
233(2)
8.4 Examples of communication applications
235(16)
8.4.1 Cognitive radio
236(12)
8.4.2 Physical-layer security
248(3)
8.5 Summary
251(4)
Part II Applications of game theory in communications and networking
9 Cellular and broadband wireless access networks
255(66)
9.1 Uplink power control in CDMA networks
257(12)
9.1.1 Single-cell CDMA networks
258(5)
9.1.2 Multi-cell wireless CDMA networks
263(6)
9.2 Resource allocation in single-cell OFDMA networks
269(10)
9.2.1 OFDMA resource-allocation model
270(2)
9.2.2 Nash bargaining solution for subcarrier allocation
272(2)
9.2.3 Algorithms for reaching the Nash bargaining solution
274(5)
9.3 Power allocation in femtocell networks
279(8)
9.3.1 Femtocell power control as a Stackelberg game
280(4)
9.3.2 Multi-leader multi-follower Stackelberg equilibrium
284(2)
9.3.3 Algorithm for reaching the Stackelberg equilibrium
286(1)
9.4 IEEE 802.16 broadband wireless access networks
287(20)
9.4.1 Resource allocation and admission control
287(12)
9.4.2 Relay-station deployment in IEEE 802.16j
299(8)
9.5 Network selection in multi-technology wireless networks
307(13)
9.5.1 Network selection as a non-cooperative game
309(2)
9.5.2 Network selection with incomplete information
311(9)
9.6 Summary
320(1)
10 Wireless local area networks
321(24)
10.1 MAC protocol design
322(4)
10.1.1 Static game
323(1)
10.1.2 Dynamic game
324(1)
10.1.3 Deviation detection and penalization
325(1)
10.1.4 Related work
326(1)
10.2 Random-access control
326(4)
10.2.1 Choice of utility function
327(1)
10.2.2 Dynamics of a random-access game
328(1)
10.2.3 Extension with propagation delay and estimation error
329(1)
10.2.4 Related work
329(1)
10.3 Rate selection for VoIP service on WLAN
330(2)
10.3.1 Game formulation
330(1)
10.3.2 Payoff function
331(1)
10.4 Access-point selection
332(5)
10.4.1 Formulation of a population game
333(2)
10.4.2 Price of anarchy
335(1)
10.4.3 Access pricing
335(1)
10.4.4 Related work
336(1)
10.5 Admission control
337(2)
10.5.1 Two-player game formulation
337(2)
10.5.2 Interpretation of payoff
339(1)
10.6 WiFi access-point pricing
339(5)
10.6.1 Pricing scheme for direct payment
340(1)
10.6.2 User with Web browsing
341(1)
10.6.3 User with file transfer
342(1)
10.6.4 Model for uncertain application
343(1)
10.7 Summary
344(1)
11 Multi-hop networks
345(30)
11.1 Routing-game basics
345(4)
11.2 Cooperation enforcement and learning using a repeated game
349(8)
11.2.1 System model and problem formulation
349(1)
11.2.2 Self-learning cooperation-enforcing framework
350(2)
11.2.3 Asynchronous network
352(1)
11.2.4 Case analysis and performance evaluations
353(4)
11.3 Hierarchical routing using a network-formation game
357(12)
11.3.1 System model and game formulation
358(4)
11.3.2 Hierarchical network-formation game solution
362(2)
11.3.3 Hierarchical network-formation algorithm
364(2)
11.3.4 Simulation results and analysis
366(3)
11.4 Other typical approaches
369(4)
11.4.1 Price-based solution
369(1)
11.4.2 Truthfulness and security using auction theory
370(2)
11.4.3 Evolutionary-game approach
372(1)
11.5 Summary
373(2)
12 Cooperative-transmission networks
375(43)
12.1 Basics of cooperative transmission
376(4)
12.1.1 Cooperative-transmission protocols
376(4)
12.1.2 State of the art and impact on different layers
380(1)
12.2 Non-cooperative game for relay selection and power control
380(9)
12.2.1 Relay-selection and power-control problem
381(1)
12.2.2 Stackelberg-game approach
382(7)
12.3 Auction-theory-based resource allocation
389(10)
12.3.1 Resource-allocation objectives
389(3)
12.3.2 Share-auction approach
392(7)
12.4 Cooperative transmission using a cooperative game in MANET
399(12)
12.4.1 Selfishness in packet-forwarding networks
400(2)
12.4.2 Cooperative transmission using a coalitional game
402(9)
12.5 Cooperative routing
411(5)
12.5.1 Cooperative-routing algorithms
412(1)
12.5.2 WiMAX IEEE 802.16j
413(3)
12.6 Summary
416(2)
13 Cognitive-radio networks
418(42)
13.1 Cooperative spectrum sensing
421(5)
13.1.1 System model
421(2)
13.1.2 Coalitional-game formulation
423(3)
13.1.3 Centralized approach and performance comparison
426(1)
13.2 Power allocation as a non-cooperative game
426(6)
13.2.1 Underlay spectrum access and power allocation
426(2)
13.2.2 Properties of the Nash equilibrium for power allocation
428(1)
13.2.3 Distributed algorithm
429(2)
13.2.4 Pigouvian taxation and social optimality
431(1)
13.2.5 Related work
432(1)
13.3 Medium access control
432(4)
13.3.1 Channel allocation
433(1)
13.3.2 Channel access
434(1)
13.3.3 Distributed algorithms
435(1)
13.4 Decentralized dynamic spectrum access
436(5)
13.4.1 Overlay dynamic spectrum access
436(2)
13.4.2 Utility function
438(1)
13.4.3 Decentralized algorithm for channel access
439(1)
13.4.4 Alternative algorithms
440(1)
13.5 Radio resource competition based on a stochastic learning game
441(5)
13.5.1 System model of radio resource competition
441(1)
13.5.2 Auction mechanism
442(1)
13.5.3 Secondary-user strategy
443(2)
13.5.4 Learning algorithm
445(1)
13.6 Cheat-proof strategies for open spectrum sharing
446(4)
13.6.1 One-shot non-cooperative game
446(1)
13.6.2 Cooperative strategy
447(1)
13.6.3 Repeated games
448(1)
13.6.4 Cheat-proof strategy
449(1)
13.7 Spectrum leasing and cooperation
450(5)
13.7.1 Game formulation with instantaneous CSI
451(3)
13.7.2 Game formulation with long-term CSI
454(1)
13.8 Service-provider competition for dynamic spectrum allocation
455(3)
13.8.1 User demand
455(2)
13.8.2 Optimal price
457(1)
13.8.3 Related work
458(1)
13.9 Summary
458(2)
14 Internet networks
460(41)
14.1 Combined flow control and routing in communication networks
462(11)
14.1.1 Single user with multiple links
463(2)
14.1.2 Multiple users with multiple parallel links
465(6)
14.1.3 Sample Nash equilibria
471(2)
14.2 Congestion control in networks with a single service provider
473(8)
14.2.1 Pricing and congestion control
474(2)
14.2.2 Non-cooperative Nash game between followers
476(2)
14.2.3 Optimal pricing policy for the service provider
478(1)
14.2.4 Network with a large number of followers
479(2)
14.3 Pricing and revenue sharing for Internet service providers
481(6)
14.3.1 Pricing game among Internet service providers
482(2)
14.3.2 Revenue-sharing strategies
484(1)
14.3.3 Distributed algorithm for finding a Nash equilibrium
485(2)
14.4 Cooperative file sharing in peer-to-peer networks
487(12)
14.4.1 Cooperative vs. non-cooperative file sharing
489(2)
14.4.2 File sharing as a coalitional game in partition form
491(2)
14.4.3 Distributed algorithm for coalition formation
493(2)
14.4.4 Coalition formation in two-peer and N-peer networks
495(4)
14.5 Summary
499(2)
References 501(29)
Index 530
Zhu Han is an Assistant Professor of Electrical and Computer Engineering at the University of Houston. He was awarded his Ph.D. in Electrical Engineering from the University of Maryland, College Park, in 2003 and worked for two years in industry as an R&D Engineer for JDSD. Dusit Niyato is an Assistant Professor in the School of Computer Engineering at the Nanyang Technological University (NTU), Singapore. He received his Ph.D. in Electrical and Computer Engineering from the University of Manitoba, Canada, in 2008. Walid Saad is a Postdoctoral Research Associate in the Department of Electrical Engineering at Princeton University. He received his Ph.D. from the University of Oslo in 2010 and previously worked at several companies in the telecommunications and IT fields. Tamer Baar is a Swanlund Chair holder and CAS Professor of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign. He is a member of the US National Academy of Engineering, a Fellow of the IEEE and IFAC, founding president of the ISDG and current president of the AACC. Are Hjųrungnes is a Professor in the Faculty of Mathematics and Natural Sciences at the University of Oslo, Norway. He is a Senior Member of the IEEE and received his Ph.D. from the Norwegian University of Science and Technology in 2000.