Game Theory and Learning for Wireless Networks
Author: Samson Lasaulce
Publisher:
Published: 2023
Total Pages: 0
ISBN-13: 9787519296766
DOWNLOAD EBOOKPosts
Game Theory in Wireless and Communication Networks
Author: Zhu Han
Publisher: Cambridge University Press
Published: 2012
Total Pages: 555
ISBN-13: 0521196965
DOWNLOAD EBOOKThis 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.
Game Theory and Learning for Wireless Networks
Author: Samson Lasaulce
Publisher: Academic Press
Published: 2011-09-19
Total Pages: 346
ISBN-13: 0123846986
DOWNLOAD EBOOKThis 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.
Game Theory for Next Generation Wireless and Communication Networks
Author: Zhu Han
Publisher: Cambridge University Press
Published: 2019-06-27
Total Pages: 509
ISBN-13: 1108417337
DOWNLOAD EBOOKA 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.
Distributed Strategic Learning for Wireless Engineers
Author: Hamidou Tembine
Publisher: CRC Press
Published: 2012-05-18
Total Pages: 498
ISBN-13: 1439876371
DOWNLOAD EBOOKAlthough 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.
Game Theory for Wireless Engineers
Author: Allen B. MacKenzie
Publisher: Springer Nature
Published: 2022-06-01
Total Pages: 76
ISBN-13: 3031016726
DOWNLOAD EBOOKThe 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.
Game Theory and Machine Learning for Cyber Security
Author: Charles A. Kamhoua
Publisher: John Wiley & Sons
Published: 2021-09-08
Total Pages: 546
ISBN-13: 1119723949
DOWNLOAD EBOOKGAME THEORY AND MACHINE LEARNING FOR CYBER SECURITY Move beyond the foundations of machine learning and game theory in cyber security to the latest research in this cutting-edge field In Game Theory and Machine Learning for Cyber Security, a team of expert security researchers delivers a collection of central research contributions from both machine learning and game theory applicable to cybersecurity. The distinguished editors have included resources that address open research questions in game theory and machine learning applied to cyber security systems and examine the strengths and limitations of current game theoretic models for cyber security. Readers will explore the vulnerabilities of traditional machine learning algorithms and how they can be mitigated in an adversarial machine learning approach. The book offers a comprehensive suite of solutions to a broad range of technical issues in applying game theory and machine learning to solve cyber security challenges. Beginning with an introduction to foundational concepts in game theory, machine learning, cyber security, and cyber deception, the editors provide readers with resources that discuss the latest in hypergames, behavioral game theory, adversarial machine learning, generative adversarial networks, and multi-agent reinforcement learning. Readers will also enjoy: A thorough introduction to game theory for cyber deception, including scalable algorithms for identifying stealthy attackers in a game theoretic framework, honeypot allocation over attack graphs, and behavioral games for cyber deception An exploration of game theory for cyber security, including actionable game-theoretic adversarial intervention detection against advanced persistent threats Practical discussions of adversarial machine learning for cyber security, including adversarial machine learning in 5G security and machine learning-driven fault injection in cyber-physical systems In-depth examinations of generative models for cyber security Perfect for researchers, students, and experts in the fields of computer science and engineering, Game Theory and Machine Learning for Cyber Security is also an indispensable resource for industry professionals, military personnel, researchers, faculty, and students with an interest in cyber security.
Cognitive Radio Networking and Security
Author: K. J. Ray Liu
Publisher: Cambridge University Press
Published: 2010-10-28
Total Pages: 618
ISBN-13: 1139492993
DOWNLOAD EBOOKWith the rapid growth of new wireless devices and applications over the past decade, the demand for wireless radio spectrum is increasing relentlessly. The development of cognitive radio networking provides a framework for making the best possible use of limited spectrum resources, and it is revolutionising the telecommunications industry. This book presents the fundamentals of designing, implementing, and deploying cognitive radio communication and networking systems. Uniquely, it focuses on game theory and its applications to various aspects of cognitive networking. It covers in detail the core aspects of cognitive radio, including cooperation, situational awareness, learning, and security mechanisms and strategies. In addition, it provides novel, state-of-the-art concepts and recent results. This is an ideal reference for researchers, students and professionals in industry who need to learn the applications of game theory to cognitive networking.
Game Theory Applications in Network Design
Author: Kim, Sungwook
Publisher: IGI Global
Published: 2014-05-31
Total Pages: 522
ISBN-13: 1466660511
DOWNLOAD EBOOKThe use of game theoretic techniques is playing an increasingly important role in the network design domain. Understanding the background, concepts, and principles in using game theory approaches is necessary for engineers in network design. Game Theory Applications in Network Design provides the basic idea of game theory and the fundamental understanding of game theoretic interactions among network entities. The material in this book also covers recent advances and open issues, offering game theoretic solutions for specific network design issues. This publication will benefit students, educators, research strategists, scientists, researchers, and engineers in the field of network design.
Federated Learning for Wireless Networks
Author: Choong Seon Hong
Publisher: Springer Nature
Published: 2022-01-01
Total Pages: 257
ISBN-13: 9811649634
DOWNLOAD EBOOKRecently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.
