Acquire the tools to address emerging challenges in modern computer networks with this multidisciplinary review of the fundamentals.

This brief explores current research on dynamic spectrum auctions, focusing on fundamental auction theory, characteristics of the spectrum market, spectrum auction architecture and possible auction mechanisms. The brief explains how dynamic spectrum auctions, which enable new users to gain spectrum access and existing spectrum owners to obtain financial benefits, can greatly improve spectrum efficiency by resolving the artificial spectrum shortage. It examines why operators and users face significant challenges due to specialty of the spectrum market and the related requirements imposed on the auction mechanism design. Concise and up-to-date, Dynamic Spectrum Auction in Wireless Communication is designed for researchers and professionals in computer science or electrical engineering. Students studying networking will also find this brief a valuable resource.

The book, while introducing readers to the auction mechanism for resource provisioning in cloud computing, also endeavors to provide structured literature on the subject. Since various models have been proposed, it will help readers to formulate the cloud resource provisioning problem using the auction approach. The book also discusses challenges for resource provisioning in detail, helping to shape future research. The target audience for this book includes computer scientists, economists, industry professionals, research scholars, and postgraduate students. Computer science readers of this book will come to see that economics-based method are quite helpful in computer science, especially for resource provisioning. Readers with a cloud computing background will come to recognize the importance of dynamic pricing, the specific benefits of auctions, and how to formulate auctions for cloud computing. Lastly, readers from the economics community will come to understand their role in cloud computing, as well as where and how they can contribute.

This Brief introduces the wireless spectrum market and discusses the current research for spectrum auctions. It covers the unique properties of spectrum auction, such as interference relationship, reusability, divisibility, composite effect and marginal effect, while also proposing how to build economic incentives into the network architecture and protocols in order to optimize the efficiency of wireless systems. Three scenarios for designing new auctions are demonstrated. First, a truthful double auction scheme for spectrum trading considering both the heterogeneous propagation properties of channels and spatial reuse is proposed. In the second scenario, a framework is designed to enable spectrum group secondary users with a limited budget. Finally, a flexible auction is created enabling operators to purchase the right amounts of spectrum at the right prices according to their users’ dynamic demands. Both concise and comprehensive, Auction Design for the Wireless Spectrum Market is suited for professionals and researchers working with wireless communications and networks. It is also a useful tool for advanced-level students interested in spectrum and networking issues.

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.

Recently 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.