* The first tutorial-style book that gives all the relevant theory at the right level of rigor, 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-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 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 Supelec, Gif-sur-Yvette, France. He is also a part-time professor in the Department of Physics at Ecole Polytechnique, Palaiseau, France.

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

Merouane Debbah is a professor at Supelec, 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 engineerBridges 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"

provides a detailed background to start working and doing research on mean-field-type control and game theory includes several numerical examples using a MatLab-based user-friendly toolbox provides analyzsis of mean-field-type control and game problems incorporating several stochastic processes, e.g., Brownian motions, Poisson jumps, and random coefficients includes several engineering applications in both continuous and discrete time, such as micro-grid energy storage, stirred tank reactor, mechanism design for evolutionary dynamics, multi-level building evacuation problem, and the COVID-19 propagation control

Although valued for its ability to allow teams to collaborate and foster coalitional behaviors among the participants, game theory's application to networking systems is not without challenges. Distributed Strategic Learning for Wireless Engineers illuminates the promise of learning in dynamic games as a tool for analyzing network evolution and underlines the potential pitfalls and difficulties likely to be encountered. Establishing the link between several theories, this book demonstrates what is needed to learn strategic interaction in wireless networks under uncertainty, randomness, and time delays. It addresses questions such as: How much information is enough for effective distributed decision making? Is having more information always useful in terms of system performance? What are the individual learning performance bounds under outdated and imperfect measurement? What are the possible dynamics and outcomes if the players adopt different learning patterns? If convergence occurs, what is the convergence time of heterogeneous learning? What are the issues of hybrid learning? How can one develop fast and efficient learning schemes in scenarios where some players have more information than the others? What is the impact of risk-sensitivity in strategic learning systems? How can one construct learning schemes in a dynamic environment in which one of the players do not observe a numerical value of its own-payoffs but only a signal of it? How can one learn "unstable" equilibria and global optima in a fully distributed manner? The book provides an explicit description of how players attempt to learn over time about the game and about the behavior of others. It focuses on finite and infinite systems, where the interplay among the individual adjustments undertaken by the different players generates different learning dynamics, heterogeneous learning, risk-sensitive learning, and hybrid dynamics.

Although valued for its ability to allow teams to collaborate and foster coalitional behaviors among the participants, game theory s application to networking systems is not without challenges. Distributed Strategic Learning for Wireless Engineers illuminates the promise of learning in dynamic games as a tool for analyzing network evolution and underlines the potential pitfalls and difficulties likely to be encountered.

Establishing the link between several theories, this book demonstrates what is needed to learn strategic interaction in wireless networks under uncertainty, randomness, and time delays. It addresses questions such as:

• How much information is enough for effective distributed decision making?
• Is having more information always useful in terms of system performance?
• What are the individual learning performance bounds under outdated and imperfect measurement?
• What are the possible dynamics and outcomes if the players adopt different learning patterns?
• If convergence occurs, what is the convergence time of heterogeneous learning?
• What are the issues of hybrid learning?
• How can one develop fast and efficient learning schemes in scenarios where some players have more information than the others?
• What is the impact of risk-sensitivity in strategic learning systems?
• How can one construct learning schemes in a dynamic environment in which one of the players do not observe a numerical value of its own-payoffs but only a signal of it?
• How can one learn "unstable" equilibria and global optima in a fully distributed manner?

The book provides an explicit description of how players attempt to learn over time about the game and about the behavior of others. It focuses on finite and infinite systems, where the interplay among the individual adjustments undertaken by the different players generates different learning dynamics, heterogeneous learning, risk-sensitive learning, and hybrid dynamics.