With the rapid development of the modern communication networks, the problem we need to solve is no longer a pure engineering issue. In various heterogeneous network scenarios, there are service providers in need of performing economic analysis on how to ensure third parties' cooperation or attract end-users. In the other way round, third parties or end-users need to evaluate the economic benefits of cooperating or using the services from different service providers. Overall, the current wireless networks are facing a problem in which there is a tight coupling of industry-specific technologies and non-technology related network externality. Contract theory, the 2014 Nobel Prize of economic science, has been widely used in industries, from banking to telecommunications. Particularly, contract theory is an efficient tool in dealing with asymmetric information between employer/seller(s) and employee/buyer(s) by introducing cooperation. In wireless networks, the employer/seller(s) and employee/buyer(s) can be of different roles depending on the scenario under consideration. Thus, there is a great potential to utilize the ideas, methods, and models of contract theory to design efficient wireless network mechanisms. Given this background, this dissertation provides a theoretical research between wireless communications, networking, and economics. Especially, different contract theory models have been applied in various wireless networks scenarios. The main contribution of this dissertation are as follows. An overview of basic concepts, classifications, and models of contract theory is provided. Furthermore, comparisons with existing economics methods in wireless networks are conducted. Applications of contract theory for wireless networks are studied. Specially, three contract theory problems: adverse selection, moral hazard, and a mixed of the two, are applied into device-to-device (D2D) communication, mobile crowdsourcing, cognitive radio network, respectively. Numerical results are provided to show that contract theory can be utilized for developing effective mechanisms for emerging wireless network scenarios such as traffic offloading, mobile crowdsourcing, as well as spectrum trading. The potential and challenges of contract theory as a tool for designing mechanisms in future wireless networks are discussed. This dissertation provides a theoretical research between wireless communications, networking, and economics, in which different contract theory models have been applied in various wireless networks scenarios. This work places a fundamental research on network economics, especially with the framework of contract theory. This research has the potential to contribute to the future of wireless networks network economics area, and have a long term effect on problems such as incentive mechanism and pricing schemes design, resource sharing and trading.

This book presents theoretical research between wireless communications, networking, and economics using the framework of contract theory. This work fills a void in the literature by closely combining contract theoretical approaches with wireless networks design problems. Topics covered include classification in contract theory, reward design, adverse selection, and moral hazard. The authors also explore incentive mechanisms for device-to-device communication in cellular networks, insurance plans for service assurance in cloud computing markets with incomplete information, multi-dimensional incentive mechanisms and tournament based incentive mechanisms in mobile crowdsourcing. Financial applications include financing contracts with adverse selection for spectrum trading in cognitive radio networks and complementary investment of infrastructure and service providers in wireless network visualization. This book offers a useful reference for engineers and researchers in the wireless communication community who seek to integrate the notions from contract theory and wireless engineering, while emphasizing on how contract theory can be applied in wireless networks. It is also suitable for advanced-level students studying information systems or communications engineering.

Discover the very latest game-theoretic approaches for designing, modeling, and optimizing emerging wireless communication networks and systems with this unique text. Providing a unified and comprehensive treatment throughout, it explains basic concepts and theories for designing novel distributed wireless networking mechanisms, describes emerging game-theoretic tools from an engineering perspective, and provides an extensive overview of recent applications. A wealth of new tools is covered - including matching theory and games with bounded rationality - and tutorial chapters show how to use these tools to solve current and future wireless networking problems in areas such as 5G networks, network virtualization, software defined networks, cloud computing, the Internet of Things, context-aware networks, green communications, and security. This is an ideal resource for telecommunications engineers, and researchers in industry and academia who are working on the design of efficient, scalable, and robust communication protocols for future wireless networks, as well as graduate students in these fields.

This book provides the fundamental knowledge of the classical matching theory problems. It builds up the bridge between the matching theory and the 5G wireless communication resource allocation problems. The potentials and challenges of implementing the semi-distributive matching theory framework into the wireless resource allocations are analyzed both theoretically and through implementation examples. Academics, researchers, engineers, and so on, who are interested in efficient distributive wireless resource allocation solutions, will find this book to be an exceptional resource.

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.

This book constitutes the refereed proceedings of the 9th International Conference on Wireless Algorithms, Systems and Applications, WASA 2014, held in Harbin, China, in June 2014. The 41 revised full papers presented together with 30 invited papers were carefully reviewed and selected from 134 submissions. The papers cover a wide range of topics including cognitive radio networks, wireless sensor networks, cyber-physical systems, distributed and localized algorithm design and analysis, information and coding theory for wireless networks, localization, mobile cloud computing, topology control and coverage, security and privacy, underwater and underground networks, vehicular networks, information processing and data management, programmable service interfaces, energy-efficient algorithms, system and protocol design, operating system and middle-ware support and experimental test-beds and models.

This volume constitutes selected papers presented at the First International Congress on Communications, Networking, and Information Systems, CNIS 2023, held in Guilin, China, in March 2023. The 8 full papers and 2 short papers presented were thoroughly reviewed and selected from the 32 submissions. The papers are organized in topical sections on ​communications and networking, and information systems and artificial intelligence.