Game Theory and Learning for Wireless Networks

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

University researchers and R&D engineers in the industry, graduate and PhD students in wireless communications

Preface

Chapter 1. A Very Short Tour of Game Theory

1.1. Introduction

1.2. A Better Understanding of the Need for Game Theory from Four Simple Examples

1.3. Representations and Classification of Games

1.4. Some Fundamental Notions of Game Theory

1.5. More about the Scope of Game Theory

Chapter 2. Playing with Equilibria in Wireless Non-Cooperative Games

2.1. Introduction

2.2. Equilibrium Existence

2.3. Equilibrium Uniqueness

2.4. Equilibrium Selection

2.5. Equilibrium Efficiency

2.6. Conclusion

Chapter 3. Moving from Static to Dynamic Games

3.1. Introduction

3.2. Repeated Games

3.3. Stochastic Games

3.4. Difference Games and Differential Games

3.5. Evolutionary Games

Chapter 4. Bayesian Games

4.1. Introduction

4.2. Bayesian Games in a Nutshell

4.3. Application to Power Control Games

Chapter 5. Partially Distributed Learning Algorithms

5.1. Introduction

5.2. Best Response Dynamics

5.3. Fictitious-Play-Based Algorithms

5.4. Learning Logit Equilibria

5.5. Games with Cost of Learning

5.6. Learning Bargaining Solutions

5.7. Summary and Concluding Remarks

Chapter 6. Fully Distributed Learning Algorithms

6.1. Introduction

6.2. The General Game-Theoretic Setting

6.3. Trial-and-Error Learning: Learning by Experimenting

6.4. Reinforcement Learning Algorithms

6.5. Regret Matching-Based Learning: Learning Correlated Equilibria

6.6. Boltzmann-Gibbs Learning Algorithms

6.7. Evolutionary Dynamics-Based Learning in Heterogeneous Networks

6.8. Learning Satisfaction Equilibrium

6.9. Summarizing Remarks and Open Issues

Chapter 7. Energy-Efficient Power Control Games

7.1. Introduction

7.2. General System Model

7.3. The One-Shot Power Control Game

7.4. Linear Pricing-Based Power Control

7.5. The Hierarchical Power Control Game

7.6. Repeated Power Control Game

7.7. Slow Power Control and Stochastic Games

7.8. Relaxing Some Information and Behavior Assumptions

Chapter 8. Power Allocation Games and Learning in MIMO Multiuser Channels*

8.1. Introduction

8.2. Power Allocation Games in MIMO Multiple Access Channels

8.3. On the Case of Power Allocation Games in Parallel Multiple Access Channels

8.4. Power Allocation Games in Parallel Interference Relay Channels

8.5. Learning Discrete Power Allocation Policies in Fast Fading MIMO Multiple Access Channels

8.6. Learning Discrete Power Allocation Policies in Slow Fading Single-User MIMO Channels

8.7. Learning Continuous Power Allocation Policies in Static Parallel Multiple Access Channels

Chapter 9. Medium Access Control Games

9.1. Introduction

9.2. Access Control Games for Multiple Access Collision Channels

9.3. Access Control Games for Multi-Receiver Networks

9.4. Multi-Stage Access Control Games with Energy Constraints

9.5. Stochastic Access Control Games

Appendices

References

Index