| Description |
1 online resource |
| Contents |
Cover -- Half-title -- Dedication -- Title -- Copyright -- Contents -- Contributors -- Preface -- I Theoretical Fundamentals -- 1 Games and mechanisms for networked systems: incentives and algorithms -- 1.1 Introduction -- 1.2 System model -- 1.3 Interference and utility function models -- 1.4 Pricing mechanisms for multi-carrier wireless systems -- 1.4.1 Net utility maximization -- 1.4.2 Alternative designer objectives -- 1.5 Learning in pricing mechanisms -- 1.6 Auction-based mechanisms -- 1.7 Discussion and open problems -- References -- 2 Competition in wireless systems via Bayesian interference games -- 2.1 Introduction -- 2.2 Static Gaussian interference games -- 2.2.1 Preliminaries -- 2.2.2 The Gaussian interference game with unknown channel gains -- 2.2.3 Bayesian Gaussian interference game -- 2.3 Sequential interference games with incomplete information -- 2.3.1 A two-stage sequential game -- 2.3.2 A sequential game with entry -- 2.4 Repeated games with entry: the reputation effect -- 2.4.1 A repeated SBGI-E game -- 2.4.2 Sequential equilibrium of the repeated game -- 2.5 Conclusion -- 2.6 Appendix -- References -- 3 Reacting to the interference field -- 3.1 Introduction -- 3.1.1 Spectrum access as a game -- 3.1.2 Cognitive access game -- 3.1.3 Mean-field game approach -- 3.1.4 Interference management in large-scale networks -- 3.1.5 Objectives -- 3.1.6 Structure of the chapter -- 3.1.7 Notations -- 3.2 Wireless model -- 3.2.1 Channel model -- 3.2.2 Mobility model -- 3.2.3 Path-loss model -- 3.2.4 Remaining energy dynamics -- 3.2.5 Queue dynamics -- 3.2.6 SINR model -- 3.3 Game-theoretic formulations -- 3.4 Reaction to the interference field -- 3.4.1 Introduction to mean-field games -- 3.4.2 The interference field -- 3.5 Mean-field stochastic game -- 3.5.1 On a game with one-and-half player -- 3.5.2 Strategies and payoffs |
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3.5.3 Mean-field equilibrium -- 3.5.4 Structure of the optimal strategy -- 3.5.5 Performance -- 3.5.6 Mean-field deterministic game -- 3.5.7 Hierarchical mean-field game -- 3.6 Discussions -- 3.7 Conclusions -- 3.8 Open issues -- Acknowledgements -- References -- 4 Walrasian model for resource allocation and transceiver designin interference networks -- 4.1 Consumer theory -- 4.1.1 Standard consumer theory -- 4.1.2 Consumer theory for utility Ü-Ýx1+Þx1x2 -- 4.1.3 Example 1: Protected and shared bands -- 4.1.4 Example 2: Two-user MISO interference channel -- 4.1.5 Example 3: Multi-carrier interference channel -- 4.1.6 Discussion and comparison of consumer models -- 4.2 Walrasian market model -- 4.2.1 Existence of a Walrasian equilibrium -- 4.2.2 Uniqueness of the Walrasian equilibrium -- 4.2.3 Convergence of a tâtonnement process -- 4.2.4 Efficiency of a Walrasian equilibrium -- 4.2.5 Example 1: Two-user protected and shared bands -- 4.2.6 Example 2: Two-user MISO interference channel -- 4.2.7 Example 3: MC interference channel -- References -- 5. Power allocation and spectrum sharing in wireless networks: an implementation theory approach -- 5.1 Introduction -- 5.1.1 Chapter organization -- 5.2 What is implementation theory? -- 5.2.1 Game forms/mechanisms -- 5.2.2 Implementation in different types of equilibria -- 5.2.3 Desirable properties of game forms -- 5.2.4 Key results on implementation theory -- 5.3 Nash implementation for social welfare maximization and weak Pareto optimality -- 5.3.1 The model (MPSA) -- 5.3.2 The power allocation and spectrum sharing problem -- 5.3.3 Constructing a game form for the decentralized power and spectrum allocation problem -- 5.3.4 Social welfare maximizing power allocation in a single frequency band -- 5.3.5 Weakly Pareto optimal power and spectrum allocation -- 5.3.6 Interpreting Nash equilibrium |
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5.3.7 Other approaches to power allocation and spectrum sharing -- 5.4 Revenue maximization -- 5.4.1 The model -- 5.4.2 Impossibility result from implementation theory -- 5.4.3 Purely spectrum allocation problem -- 5.4.4 Purely power allocation problem -- 5.4.5 Other models and approaches on revenue maximization -- 5.5 Conclusion and reflections -- References -- 6 Performance and convergence of multi-user online learning -- 6.1 Introduction -- 6.2 Related work -- 6.3 Problem formulation and preliminaries -- 6.3.1 Factors determining the channel quality/reward -- 6.3.2 Channel models -- 6.3.3 The set of optimal allocations -- 6.3.4 Performance measure -- 6.3.5 Degree of decentralization -- 6.4 Main results -- 6.5 Achievable performance with no feedback and iid channels -- 6.6 Achievable performance with partial feedback and iid channels -- 6.7 Achievable performance with partial feedback and synchronization for iid and Markovian channels -- 6.7.1 Analysis of the regret of DLOE -- 6.7.2 Regret analysis for iid channels -- 6.7.3 Regret analysis for Markovian channels -- 6.8 Discussion -- 6.8.1 Strategic considerations -- 6.8.2 Multiple optimal allocations -- 6.8.3 Unknown suboptimality gap -- Acknowledgements -- References -- 7 Game-theoretic solution concepts and learning algorithms -- 7.1 Introduction -- 7.2 A general dynamic spectrum access game -- 7.3 Solutions concepts and dynamic spectrum access -- 7.3.1 Nash equilibrium -- 7.3.2 Epsilon -- Nash equilibrium -- 7.3.3 Satisfaction equilibrium and efficient satisfaction equilibrium -- 7.3.4 Generalized Nash equilibrium -- 7.3.5 Coarse correlated equilibrium and correlated equilibrium -- 7.3.6 Robust equilibrium -- 7.3.7 Bayesian equilibrium and augmented equilibrium -- 7.3.8 Evolutionary stable solutions -- 7.3.9 Pareto optimal action profiles and social optimal action profiles |
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7.3.10 Other equilibrium concepts -- 7.4 Learning equilibria -- 7.4.1 Learning Nash equilibria -- 7.4.2 Learning epsilon-equilibrium -- 7.4.3 Learning coarse correlated equilibrium -- 7.4.4 Learning satisfaction equilibrium -- 7.4.5 Discussion -- 7.5 Conclusion -- References -- II Cognitive radio and sharing of unlicensed spectrum -- 8 Cooperation in cognitiveradio networks: from accessto monitoring -- 8.1 Introduction -- 8.1.1 Cooperation in cognitive radio: mutual benefits and costs -- 8.2 An overview of coalitional game theory -- 8.3 Cooperative spectrum exploration and exploitation -- 8.3.1 Motivation -- 8.3.2 Basic problem -- 8.3.3 Joint sensing and access as a cooperative game -- 8.3.4 Coalition formation algorithm for joint sensing and access -- 8.3.5 Numerical results -- 8.4 Cooperative primary user activity monitoring -- 8.4.1 Motivation -- 8.4.2 Primary user activity monitoring: basic model -- 8.4.3 Cooperative primary user monitoring -- 8.4.4 Numerical results -- 8.5 Summary -- Acknowledgements -- Copyright notice -- References -- 9 Cooperative cognitive radios with diffusion networks -- 9.1 Introduction -- 9.2 Preliminaries -- 9.2.1 Basic tools in convex and matrix analysis -- 9.2.2 Graphs -- 9.3 Distributed spectrum sensing -- 9.4 Iterative consensus-based approaches -- 9.4.1 Average consensus algorithms -- 9.4.2 Acceleration techniques for iterative consensus algorithms -- 9.4.3 Empirical evaluation -- 9.5 Consensus techniques based on CoMAC -- 9.6 Adaptive distributed spectrum sensing based on adaptive subgradient techniques -- 9.6.1 Distributed detection with adaptive filters -- 9.6.2 Set-theoretic adaptive filters for distributed detection -- 9.6.3 Empirical evaluation -- 9.7 Channel probing -- 9.7.1 Introduction -- 9.7.2 Admissibility problem -- 9.7.3 Power and admission control algorithms |
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9.7.4 Channel probing for admission control -- 9.7.5 Conclusions -- Acknowledgements -- References -- 10 Capacity scaling limits of cognitive multiple access networks -- 10.1 Introduction -- 10.2 Organization and notation -- 10.3 Three main cognitive radio paradigms -- 10.4 Power allocation in cognitive radio networks -- 10.4.1 Point-to-point time-invariant cognitive radio channels -- 10.4.2 Point-to-point time-varying cognitive radio channels -- 10.4.3 Fading multiple access cognitive radio channels -- 10.5 Capacity scaling with full CSI: homogeneous CoEs -- 10.6 Capacity scaling with full CSI: heterogeneous CoEs -- 10.7 Capacity scaling with generalized fading distributions -- 10.8 Capacity scaling with reduced CSI -- 10.9 Capacity scaling in distributed cognitive multiple access networks -- 10.10 Summary and conclusions -- Acknowledgements -- References -- 11 Dynamic resource allocation in cognitive radio relay networks using sequential auctions -- 11.1 Introduction -- 11.1.1 Cognitive radio relay network -- 11.1.2 Sequential auctions -- 11.1.3 Chapter outline -- 11.2 System model and problem formulation -- 11.2.1 System model of cognitive radio relay network -- 11.2.2 Bandwidth allocation problem and optimal solution -- 11.3 Auction formulation and sequential auctions -- 11.3.1 Auction formulation -- 11.3.2 Sequential first-price auction -- 11.3.3 Sequential second-price auction -- 11.3.4 Example -- 11.4 Simulation results -- 11.4.1 Total transmission rate -- 11.4.2 Feedback and complexity -- 11.4.3 Fairness -- 11.5 Conclusions -- References -- 12 Incentivized secondary coexistence -- 12.1 Introduction -- 12.2 System model and bandwidth exchange -- 12.2.1 System model -- 12.2.2 Bandwidth exchange -- 12.3 Database assisted Nash bargaining for bandwidth exchange -- 12.3.1 Using database to obtain bargaining parameters |
| Summary |
"Presenting state-of-the-art research into methods of wireless spectrum allocation based on game theory and mechanism design, this innovative and comprehensive book provides a strong foundation for the design of future wireless mechanisms and spectrum markets. Prominent researchers showcase a diverse range of novel insights and approaches to the increasing demand for limited spectrum resources, with a consistent emphasis on theoretical methods, analytical results and practical examples. Covering fundamental underlying principles, licensed spectrum sharing, opportunistic spectrum sharing, and wider technical and economic considerations, this singular book will be of interest to academic and industrial researchers, wireless industry practitioners, and regulators interested in the foundations of cutting-edge spectrum management"-- Provided by publisher |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Wireless communication systems -- Management.
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Radio frequency allocation.
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Signal theory (Telecommunication)
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Game theory.
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Game Theory
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Game theory.
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Radio frequency allocation.
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Signal theory (Telecommunication)
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Wireless communication systems -- Management.
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| Form |
Electronic book
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| Author |
Alpcan, Tansu, 1975- editor.
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| ISBN |
9781139524421 |
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1139524429 |
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9781107702899 |
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1107702895 |
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9781107598331 |
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1107598338 |
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