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

This unified 2001 treatment of game theory focuses on finding state-of-the-art solutions to issues surrounding the next generation of wireless and communications networks. The key results and tools of game theory are covered, as are various real-world technologies and a wide range of techniques for modeling, design and analysis.

The application of mathematical analysis to wireless networks has met with limited success, due to the complexity of mobility and traffic models, coupled with the dynamic topology and the unpredictability of link quality that characterize such networks. The ability to model individual, independent decision makers whose actions potentially affect all other decision makers makes game theory particularly attractive to analyze the performance of ad hoc networks. Game theory is a field of applied mathematics that describes and analyzes interactive decision situations. It consists of a set of analytical tools that predict the outcome of complex interactions among rational entities, where rationality demands a strict adherence to a strategy based on perceived or measured results. In the early to mid-1990's, game theory was applied to networking problems including flow control, congestion control, routing and pricing of Internet services. More recently, there has been growing interest in adopting game-theoretic methods to model today's leading communications and networking issues, including power control and resource sharing in wireless and peer-to-peer networks. This work presents fundamental results in game theory and their application to wireless communications and networking. We discuss normal-form, repeated, and Markov games with examples selected from the literature. We also describe ways in which learning can be modeled in game theory, with direct applications to the emerging field of cognitive radio. Finally, we discuss challenges and limitations in the application of game theory to the analysis of wireless systems. We do not assume familiarity with game theory. We introduce major game theoretic models and discuss applications of game theory including medium access, routing, energy-efficient protocols, and others. We seek to provide the reader with a foundational understanding of the current research on game theory applied to wireless communications and networking.

A unified treatment of the latest game theoretic approaches for designing, modeling, and optimizing emerging wireless communication networks. Covering theory, analytical tools, and applications, it is ideal for researchers and graduate students in academia and industry designing efficient, scalable and robust protocols for future wireless networks.

Used to explain complicated economic behavior for decades, game theory is quickly becoming a tool of choice for those serious about optimizing next generation wireless systems. Illustrating how game theory can effectively address a wide range of issues that until now remained unresolved, Game Theory for Wireless Communications and Networking provides a systematic introduction to the application of this powerful and dynamic tool. This comprehensive technical guide explains game theory basics, architectures, protocols, security, models, open research issues, and cutting-edge advances and applications. It describes how to employ game theory in infrastructure-based wireless networks and multihop networks to reduce power consumption—while improving system capacity, decreasing packet loss, and enhancing network resilience. Providing for complete cross-referencing, the text is organized into four parts: Fundamentals—introduces the fundamental issues and solutions in applying different games in different wireless domains, including wireless sensor networks, vehicular networks, and OFDM-based wireless systems Power Control Games—considers issues and solutions in power control games Economic Approaches—reviews applications of different economic approaches, including bargaining and auction-based approaches Resource Management—explores how to use the game theoretic approach to address radio resource management issues The book explains how to apply the game theoretic model to address specific issues, including resource allocation, congestion control, attacks, routing, energy management, packet forwarding, and MAC. Facilitating quick and easy reference to related optimization and algorithm methodologies, it supplies you with the background and tools required to use game theory to drive the improvement and development of next generation wireless systems.

The application of mathematical analysis to wireless networks has met with limited success, due to the complexity of mobility and traffic models, coupled with the dynamic topology and the unpredictability of link quality that characterize such networks. The ability to model individual, independent decision makers whose actions potentially affect all other decision makers makes game theory particularly attractive to analyze the performance of ad hoc networks. Game theory is a field of applied mathematics that describes and analyzes interactive decision situations. It consists of a set of analytical tools that predict the outcome of complex interactions among rational entities, where rationality demands a strict adherence to a strategy based on perceived or measured results. In the early to mid-1990's, game theory was applied to networking problems including flow control, congestion control, routing and pricing of Internet services. More recently, there has been growing interest in adopting game-theoretic methods to model today's leading communications and networking issues, including power control and resource sharing in wireless and peer-to-peer networks. This work presents fundamental results in game theory and their application to wireless communications and networking. We discuss normal-form, repeated, and Markov games with examples selected from the literature. We also describe ways in which learning can be modeled in game theory, with direct applications to the emerging field of cognitive radio. Finally, we discuss challenges and limitations in the application of game theory to the analysis of wireless systems. We do not assume familiarity with game theory. We introduce major game theoretic models and discuss applications of game theory including medium access, routing, energy-efficient protocols, and others. We seek to provide the reader with a foundational understanding of the current research on game theory applied to wireless communications and networking.

This is the first comprehensive tutorial on game theory and its application to wireless communications. The book starts with a guide to the essential principles of game theory relevant to the communications engineer, giving tools that can be used to develop applications in wireless communications. It explains how game theory models can be applied to distributed resource allocation in a perfect world. Having clarified how the models can be applied in principle, the book then gives practical implementation methods for the real world, showing how the models in the perfect world need to be adapted to real life situations which are far from perfect. 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 popularity of smart phones and other mobile devices has brought about major expansion in the realm of wireless communications. With this growth comes the need to improve upon network capacity and overall user experience, and game-based methods can offer further enhancements in this area. Game Theory Framework Applied to Wireless Communication Networks is a pivotal reference source for the latest scholarly research on the application of game-theoretic approaches to enhance wireless networking. Featuring prevailing coverage on a range of topics relating to the advanced game model, mechanism designs, and effective equilibrium concepts, this publication is an essential reference source for researchers, students, technology developers, and engineers. This publication features extensive, research-based chapters across a broad scope of relevant topics, including potential games, coalition formation game, heterogeneous networks, radio resource allocation, coverage optimization, distributed dynamic resource allocation, dynamic spectrum access, physical layer security, and cooperative video transmission.

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.