Walid Saad – författare

Comprehensive overview of the principles, theories, and techniques needed to build end-to-end semantic communication systems, with case studies included.

In this rapidly evolving landscape, the integration of connected intelligence applications highlights the pressing need for networks to gain intelligence in a non-siloed and ad hoc manner. The traditional incremental approach to network design is no longer sufficient to support the diverse and dynamic requirements of these emerging applications. This necessitates a paradigm shift towards more intelligent and adaptive network architectures.

From theory to application, Foundations of Semantic Communication Networks describes and provides a comprehensive understanding of everything needed to build end-to-end semantic communication systems. This book covers various interdisciplinary topics such as the mathematical foundations of semantic communications, information theoretical perspectives, joint-source channel coding, semantic-aware resource management strategies, interoperability under heterogeneous semantic communication users, advanced artificial intelligence (AI) and machine reasoning techniques for enabling connected intelligent applications, secure and privacy-preserving semantic communication systems, and the coexistence and interoperability of semantic, goal-oriented, and legacy systems.

The book examines unique features of end-to-end networking with semantic communications, including instilling reasoning behaviors in communication nodes, the role of the semantic plane in information filtering, control of communication and computing resources, transmit and receive signaling schemes, and connected intelligence device control. It emphasizes the importance of data semantics and age of information metrics. The book also discusses the profound impact of semantic communications on the telecom industry, highlighting changes in network performance, resource management, traffic, as well as spectral and energy efficiency.

Furthermore, the book provides insights into the mathematical constructs and AI theories for formulating semantic information, such as topology and category theory. It explores real-world applications, case studies, and future research directions as wireless technologies transition to 6G and beyond.

Written by four recognized experts in the field with a wealth of expertise from academia, industry, and research institutions, Foundations of Semantic Communication Networks addresses sample topics, including:

Foundations of Semantic Communication Networks is an excellent forward-thinking resource on the subject for readers with a strong background in the subject matter, including graduate-level students, academics, practitioners, and industry researchers.

Comprehensive overview of the principles, theories, and techniques needed to build end-to-end semantic communication systems, with case studies included.

In this rapidly evolving landscape, the integration of connected intelligence applications highlights the pressing need for networks to gain intelligence in a non-siloed and ad hoc manner. The traditional incremental approach to network design is no longer sufficient to support the diverse and dynamic requirements of these emerging applications. This necessitates a paradigm shift towards more intelligent and adaptive network architectures.

From theory to application, Foundations of Semantic Communication Networks describes and provides a comprehensive understanding of everything needed to build end-to-end semantic communication systems. This book covers various interdisciplinary topics such as the mathematical foundations of semantic communications, information theoretical perspectives, joint-source channel coding, semantic-aware resource management strategies, interoperability under heterogeneous semantic communication users, advanced artificial intelligence (AI) and machine reasoning techniques for enabling connected intelligent applications, secure and privacy-preserving semantic communication systems, and the coexistence and interoperability of semantic, goal-oriented, and legacy systems.

The book examines unique features of end-to-end networking with semantic communications, including instilling reasoning behaviors in communication nodes, the role of the semantic plane in information filtering, control of communication and computing resources, transmit and receive signaling schemes, and connected intelligence device control. It emphasizes the importance of data semantics and age of information metrics. The book also discusses the profound impact of semantic communications on the telecom industry, highlighting changes in network performance, resource management, traffic, as well as spectral and energy efficiency.

Furthermore, the book provides insights into the mathematical constructs and AI theories for formulating semantic information, such as topology and category theory. It explores real-world applications, case studies, and future research directions as wireless technologies transition to 6G and beyond.

Written by four recognized experts in the field with a wealth of expertise from academia, industry, and research institutions, Foundations of Semantic Communication Networks addresses sample topics, including:

Foundations of Semantic Communication Networks is an excellent forward-thinking resource on the subject for readers with a strong background in the subject matter, including graduate-level students, academics, practitioners, and industry researchers.

This book constitutes the refereed proceedings of the 5th International Conference on Decision and Game Theory for Security, GameSec 2014, held in Los Angeles, CA, USA, in November 2014.

The 16 revised full papers presented together with 7 short papers were carefully reviewed and selected from numerous submissions. The covered topics cover multiple facets of cyber security that include: rationality of adversary, game-theoretic cryptographic techniques, vulnerability discovery and assessment, multi-goal security analysis, secure computation, economic-oriented security, and surveillance for security. Those aspects are covered in a multitude of domains that include networked systems, wireless communications, border patrol security, and control systems.

This brief introduces overlapping coalition formation games (OCF games), a novel mathematical framework from cooperative game theory that can be used to model, design and analyze cooperative scenarios in future wireless communication networks.

The concepts of OCF games are explained, and several algorithmic aspects are studied.  In addition, several major application scenarios are discussed. These applications are drawn from a variety of fields that include radio resource allocation in dense wireless networks, cooperative spectrum sensing for cognitive radio networks, and resource management for crowd sourcing. For each application, the use of OCF games is discussed in detail in order to show how this framework can be used to solve relevant wireless networking problems.

Overlapping Coalition Formation Games in Wireless Communication Networks provides researchers, students and practitioners with a concise overview of existing works in this emerging area,exploring the relevant fundamental theories, key techniques, and significant applications. 

This book constitutes the refereed proceedings of the 6th International Conference on Game Theory for Networks, GameNets 2016, held in Kelowna, Canada, in May 2016. The 13 papers were carefully selected from 26 submissions and cover topics such as algorithmic game theory, game models and theories, game theories in wireless networks, design and analysis of economic games. 

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 optimizationtheory, 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.