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Game Theory and Learning for Wireless Networks

Book Description

Written by leading experts in the field, Game Theory and Learning for Wireless Networks Covers how theory can be used to solve prevalent problems in wireless networks such as power control, resource allocation or medium access control. With the emphasis now on promoting ‘green’ solutions in the wireless field where power consumption is minimized, there is an added focus on developing network solutions that maximizes the use of the spectrum available.

With the growth of distributed wireless networks such as Wi-Fi and the Internet; the push to develop ad hoc and cognitive networks has led to a considerable interest in applying game theory to wireless communication systems. Game Theory and Learning for Wireless Networks is the first comprehensive resource of its kind, and is ideal for wireless communications R&D engineers and graduate students.

Samson Lasaulce is a senior CNRS researcher at the Laboratory of Signals and Systems (LSS) at Supélec, Gif-sur-Yvette, France. He is also a part-time professor in the Department of Physics at École Polytechnique, Palaiseau, France.

Hamidou Tembine is a professor in the Department of Telecommunications at Supélec, Gif-sur-Yvette, France.

Merouane Debbah is a professor at Supélec, Gif-sur-Yvette, France. He is the holder of the Alcatel-Lucent chair in flexible radio since 2007.

  • The first tutorial style book that gives all the relevant theory, at the right level of rigour, for the wireless communications engineer
  • Bridges the gap between theory and practice by giving examples and case studies showing how game theory can solve real world resource allocation problems
  • Contains algorithms and techniques to implement game theory in wireless terminals

Table of Contents

  1. Cover image
  2. Table of Contents
  3. Front Matter
  4. Copyright
  5. Preface
  6. Chapter 1. A Very Short Tour of Game Theory
  7. 1.1. Introduction
  8. 1.2. A Better Understanding of the Need for Game Theory from Four Simple Examples
  9. 1.3. Representations and Classification of Games
  10. 1.4. Some Fundamental Notions of Game Theory
  11. 1.5. More about the Scope of Game Theory
  12. Chapter 2. Playing with Equilibria in Wireless Non-Cooperative Games
  13. 2.1. Introduction
  14. 2.2. Equilibrium Existence
  15. 2.3. Equilibrium Uniqueness
  16. 2.4. Equilibrium Selection
  17. 2.5. Equilibrium Efficiency
  18. 2.6. Conclusion
  19. Chapter 3. Moving from Static to Dynamic Games
  20. 3.1. Introduction
  21. 3.2. Repeated Games
  22. 3.3. Stochastic Games
  23. 3.4. Difference Games and Differential Games
  24. 3.5. Evolutionary Games
  25. Chapter 4. Bayesian Games
  26. 4.1. Introduction
  27. 4.2. Bayesian Games in a Nutshell
  28. 4.3. Application to Power Control Games
  29. Chapter 5. Partially Distributed Learning Algorithms
  30. 5.1. Introduction
  31. 5.2. Best Response Dynamics
  32. 5.3. Fictitious-Play-Based Algorithms
  33. 5.4. Learning Logit Equilibria
  34. 5.5. Games with Cost of Learning
  35. 5.6. Learning Bargaining Solutions
  36. 5.7. Summary and Concluding Remarks
  37. Chapter 6. Fully Distributed Learning Algorithms
  38. 6.1. Introduction
  39. 6.2. The General Game-Theoretic Setting
  40. 6.3. Trial-and-Error Learning: Learning by Experimenting
  41. 6.4. Reinforcement Learning Algorithms
  42. 6.5. Regret Matching-Based Learning: Learning Correlated Equilibria
  43. 6.6. Boltzmann-Gibbs Learning Algorithms
  44. 6.7. Evolutionary Dynamics-Based Learning in Heterogeneous Networks
  45. 6.8. Learning Satisfaction Equilibrium
  46. 6.9. Summarizing Remarks and Open Issues
  47. Chapter 7. Energy-Efficient Power Control Games
  48. 7.1. Introduction
  49. 7.2. General System Model
  50. 7.3. The One-Shot Power Control Game
  51. 7.4. Linear Pricing-Based Power Control
  52. 7.5. The Hierarchical Power Control Game
  53. 7.6. Repeated Power Control Game
  54. 7.7. Slow Power Control and Stochastic Games
  55. 7.8. Relaxing Some Information and Behavior Assumptions
  56. Chapter 8. Power Allocation Games and Learning in MIMO Multiuser Channels*
  57. 8.1. Introduction
  58. 8.2. Power Allocation Games in MIMO Multiple Access Channels
  59. 8.3. On the Case of Power Allocation Games in Parallel Multiple Access Channels
  60. 8.4. Power Allocation Games in Parallel Interference Relay Channels
  61. 8.5. Learning Discrete Power Allocation Policies in Fast Fading MIMO Multiple Access Channels
  62. 8.6. Learning Discrete Power Allocation Policies in Slow Fading Single-User MIMO Channels
  63. 8.7. Learning Continuous Power Allocation Policies in Static Parallel Multiple Access Channels
  64. Chapter 9. Medium Access Control Games
  65. 9.1. Introduction
  66. 9.2. Access Control Games for Multiple Access Collision Channels
  67. 9.3. Access Control Games for Multi-Receiver Networks
  68. 9.4. Multi-Stage Access Control Games with Energy Constraints
  69. 9.5. Stochastic Access Control Games
  70. Appendices
  71. References
  72. Index