Hamidou Tembine – författare

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 Sup�lec, Gif-sur-Yvette, France. He is also a part-time professor in the Department of Physics at �cole Polytechnique, Palaiseau, France.

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

Merouane Debbah is a professor at Sup�lec, 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

The contents of this book comprise an appropriate background to start working and doing research on mean-field-type control and game theory. To make the exposition and explanation even easier, we first study the deterministic optimal control and differential linear-quadratic games. Then, we progressively add complexity step-by-step and little-by-little to the problem settings until we finally study and analyze mean-field-type control and game problems incorporating several stochastic processes, e.g., Brownian motions, Poisson jumps, and random coefficients.We go beyond the Nash equilibrium, which provides a solution for non- cooperative games, by analyzing other game-theoretical concepts such as the Berge, Stackelberg, adversarial/robust, and co-opetitive equilibria. For the mean-field-type game analysis, we provide several numerical examples using a Matlab-based user-friendly toolbox that is available for the free use to the readers of this book.We present several engineering applications in both continuous and discrete time. Among these applications we find the following: water distribution systems, micro-grid energy storage, stirred tank reactor, mechanism design for evolutionary dynamics, multi-level building evacuation problem, and the COVID-19 propagation control."With such a demand from engineering audiences, this book is very timely and provides a thorough study of mean-field-type game theory. The strenuous protagonist of this book is to bridge between the theoretical findings and engineering solutions. The book introduces the basics first, and then mathematical frameworks are elaborately explained. The engineering application examples are shown in detail, and the popular learning approaches are also investigated. Those advantageous characteristics will make this book a comprehensive handbook of many engineering fields for many years, and I will buy one when it gets published."- Zhu Han

The contents of this book comprise an appropriate background to start working and doing research on mean-field-type control and game theory. To make the exposition and explanation even easier, we first study the deterministic optimal control and differential linear-quadratic games. Then, we progressively add complexity step-by-step and little-by-little to the problem settings until we finally study and analyze mean-field-type control and game problems incorporating several stochastic processes, e.g., Brownian motions, Poisson jumps, and random coefficients.We go beyond the Nash equilibrium, which provides a solution for non- cooperative games, by analyzing other game-theoretical concepts such as the Berge, Stackelberg, adversarial/robust, and co-opetitive equilibria. For the mean-field-type game analysis, we provide several numerical examples using a Matlab-based user-friendly toolbox that is available for the free use to the readers of this book.We present several engineering applications in both continuous and discrete time. Among these applications we find the following: water distribution systems, micro-grid energy storage, stirred tank reactor, mechanism design for evolutionary dynamics, multi-level building evacuation problem, and the COVID-19 propagation control."With such a demand from engineering audiences, this book is very timely and provides a thorough study of mean-field-type game theory. The strenuous protagonist of this book is to bridge between the theoretical findings and engineering solutions. The book introduces the basics first, and then mathematical frameworks are elaborately explained. The engineering application examples are shown in detail, and the popular learning approaches are also investigated. Those advantageous characteristics will make this book a comprehensive handbook of many engineering fields for many years, and I will buy one when it gets published."- Zhu Han

This groundbreaking book is the first to establish a clear and comprehensive link between game theory and deep learning, demonstrating the critical importance of this interplay for solving key technological challenges such as those found in future wireless and energy networks. It delves into the latest connections between game theory and generative AI, including large language models, showcasing how these advanced concepts can be harnessed to address complex real-world problems. Readers will gain a deep understanding of how these two powerful fields intersect and the practical applications of this knowledge, making it an essential resource for anyone looking to stay at the forefront of technological innovation.With this book the reader will be able to: Develop a solid foundation in game theory and understand interactive scenarios in both engineering and everyday life; Effectively apply their knowledge to practical problems, including resource allocation, security, and influence maximization; Design strategies that optimally exploit available information through successive optimization, reinforcement learning, deep learning, and generative AI techniques.Gives applications of game theory and deep learning across various domains, such as future wireless and energy networksExplores the burgeoning connections between game theory and generative AI, including large language models, providing readers with insights into the most current technological advancementsGives practical methodologies for studying games and to develop design strategies, supported by numerous examples and detailed case studiesAn in-depth overview of outstanding research challenges and potential future directions in the interplay between game theory and deep learning, helping readers stay ahead in the field and identify new areas for exploration and innovationGuidance on implementation with code snippets and a detailed presentation of methods

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.

Mean-Field-Type Game Theory I is the first of two volumes that together form a comprehensive treatment of mean-field-type game theory and applications focused on finding state-of-the-art solutions to issues surrounding the next generation of cloud social networking, smart energy systems, transportation and wireless networks. The text shows how mean-field-type game theory provides the ideal framework for designing robust, accurate and efficient algorithms for the autonomous and distributed architectures on which future cities and networks will rely to improve the efficiency and flexibility, security and quality of life. This first volume enables readers to develop a solid understanding of mean-field-type game theory. It covers key theoretical results such as the stochastic maximum principle and dynamic programming in both discrete and continuous time. The book also covers a wide range of techniques for modeling, designing and analyzing risk and uncertainties using game theory, as well as state-of-the-art distributed mean-field learning algorithm techniques.Mean-Field-Type Game Theory I: Foundations and New Directions is an ideal resource for academic researchers, and advanced undergraduate and graduate students, surveying basic ideas and advanced topics.