Game Theory and Learning for Wireless Networks: Fundamentals and Applications

Academic Press
2
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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
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About the author

Samson received his BSc and Agrégation degree in Applied Physics from École Normale Supérieure (Cachan) and his MSc and PhD in Signal Processing from École Nationale Supérieure des Télécommunications (Paris). Professional experience. He has been working with Motorola Labs for three years (1999, 2000, 2001) and with France Télécom R&D for two years (2002, 2003). Since 2004, he has joined the CNRS and Supélec. Since 2004, he is also Chargé d'Enseignement at École Polytechnique. His broad interests lie in the areas of communications, signal processing and information theory with a special emphasis on game theory for wireless communications. Samson Lasaulce is the recipient of the 2007 ACM/ICST International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS) and 2009 International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM) best student paper awards. Elena Veronica Belmega, who is a PhD student under my supervision, was awarded with the 2009 Academy of Sciences - L'Oreal - Unesco award for young women in sciences in France.

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Reviews

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2 total
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Additional Information

Publisher
Academic Press
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Published on
Sep 19, 2011
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Pages
336
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ISBN
9780123846990
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Language
English
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Genres
Mathematics / Game Theory
Technology & Engineering / Mobile & Wireless Communications
Technology & Engineering / Telecommunications
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Content Protection
This content is DRM protected.
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Available on Android devices
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Hamidou Tembine
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.

Nick Bostrom
The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but we have cleverer brains. If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species then would come to depend on the actions of the machine superintelligence. But we have one advantage: we get to make the first move. Will it be possible to construct a seed AI or otherwise to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation? To get closer to an answer to this question, we must make our way through a fascinating landscape of topics and considerations. Read the book and learn about oracles, genies, singletons; about boxing methods, tripwires, and mind crime; about humanity's cosmic endowment and differential technological development; indirect normativity, instrumental convergence, whole brain emulation and technology couplings; Malthusian economics and dystopian evolution; artificial intelligence, and biological cognitive enhancement, and collective intelligence. This profoundly ambitious and original book picks its way carefully through a vast tract of forbiddingly difficult intellectual terrain. Yet the writing is so lucid that it somehow makes it all seem easy. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time.
Hamidou Tembine
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.

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