Game Theory and Learning for Wireless Networks
Fundamentals and Applications
- Samson Lasaulce, Supelec, France
- Hamidou Tembine, CNRS LSS, Gif-sur-Yvette, France
* Bridges the gap between theory and practice by giving examples and case studies showing how game theory can solve real-word problems.
* Contains algorithms and techniques to implement game theory in wireless terminals.
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 âgreenâ 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 SupÃ©lec, Gif-sur-Yvette, France. He is also a part-time professor in the Department of Physics at Ãcole Polytechnique, Palaiseau, France.
Hamidou Tembine is a professor in the Department of Telecommunications at SupÃ©lec, Gif-sur-Yvette, France.
Merouane Debbah is a professor at SupÃ©lec, Gif-sur-Yvette, France. He is the holder of the Alcatel-Lucent chair in flexible radio since 2007.
AudienceUniversity researchers and R&D engineers in the industry, graduate and PhD students in wireless communications
- Published: August 2011
- Imprint: ACADEMIC PRESS
- ISBN: 978-0-12-384698-3
Table of Contents
Preface and Introduction.
Part A Games with Complete Information
A1 A short tour of game theory
A2 Playing with equilibria in wireless non-cooperative games
A3 Moving from static to dynamic game
A4 Coalitional games
Part B Games with complete information and learning
B1 Bayesian games
B2 Partially distributed learning algorithms
B3 Fully distributed learning algorithms
Part C Case Studies
C1 Fundamentals of wireless communications
C2 Energy-efficient power control games
C3 Rate-efficient power allocation games
C4 Medium access control games
Part D Appendices
Bibliography and index