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Mechanisms and games for dynamic spectrum allocation /
"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 d...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, United Kingdom :
Cambridge University Press,
2013.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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.