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Comprehensive overview of the principles, theories, and techniques behind deep learning enabled semantic communications Deep Learning Enabled Semantic Communications explores the synergy between deep learning and semantic communication, particularly in the context of advancing 6G networks. It provides a focused introduction to the subject, systematically covering deep learning enabled semantic communication systems and task-oriented semantic transmission paradigms in wireless communication. The book reviews various aspects of semantic communications, including information theory, multimodal technologies, semantic noise, and semantic sensing. It explores cutting-edge semantic communication architectures, highlighting their advantages over traditional approaches and their potential to drive the future of intelligent information industry. The book also details applications of deep learning-based semantic communication systems across various sources, including text, speech, images, and videos, comprehensively addressing system design, performance optimization, and measurement metrics. The book is divided into eight main parts, which cover foundational knowledge, system design, multimodal and multitask-oriented semantic communication systems, joint semantic sensing and sampling, semantic noise suppression, and generative AI enabled systems. Written by a diverse group of experts in academia and research institutions, Deep Learning Enabled Semantic Communications includes information on: Fundamental knowledge about deep learning and semantic communications, including the history, neural networks, and semantic information theoryCompression of multimodal inputs, extraction of global semantic information, and the design of neural networks to boost the capability of handling lengthy speechIncorporation of different sources to extract semantic features and serve diverse intelligent tasks at the receiverIntroduction of semantic impairments in communications to uncover how to design robust systemsJoint design of data sampling, compression, and coding schemes under the guidance of semantic informationFramework of generative semantic communications to detail the principles of incorporating generative models into semantic communicationsDeep Learning Enabled Semantic Communications is an essential learning resource and reference for graduate and undergraduate students pursuing degrees in wireless communications, signal processing, or deep learning as well as engineers in the telecommunications and IT industries focusing on wireless communication techniques.

Comprehensive overview of the principles, theories, and techniques behind deep learning enabled semantic communications Deep Learning Enabled Semantic Communications explores the synergy between deep learning and semantic communication, particularly in the context of advancing 6G networks. It provides a focused introduction to the subject, systematically covering deep learning enabled semantic communication systems and task-oriented semantic transmission paradigms in wireless communication. The book reviews various aspects of semantic communications, including information theory, multimodal technologies, semantic noise, and semantic sensing. It explores cutting-edge semantic communication architectures, highlighting their advantages over traditional approaches and their potential to drive the future of intelligent information industry. The book also details applications of deep learning-based semantic communication systems across various sources, including text, speech, images, and videos, comprehensively addressing system design, performance optimization, and measurement metrics. The book is divided into eight main parts, which cover foundational knowledge, system design, multimodal and multitask-oriented semantic communication systems, joint semantic sensing and sampling, semantic noise suppression, and generative AI enabled systems. Written by a diverse group of experts in academia and research institutions, Deep Learning Enabled Semantic Communications includes information on: Fundamental knowledge about deep learning and semantic communications, including the history, neural networks, and semantic information theory Compression of multimodal inputs, extraction of global semantic information, and the design of neural networks to boost the capability of handling lengthy speech Incorporation of different sources to extract semantic features and serve diverse intelligent tasks at the receiver Introduction of semantic impairments in communications to uncover how to design robust systems Joint design of data sampling, compression, and coding schemes under the guidance of semantic information Framework of generative semantic communications to detail the principles of incorporating generative models into semantic communications Deep Learning Enabled Semantic Communications is an essential learning resource and reference for graduate and undergraduate students pursuing degrees in wireless communications, signal processing, or deep learning as well as engineers in the telecommunications and IT industries focusing on wireless communication techniques.

Comprehensive overview of the principles, theories, and techniques behind deep learning enabled semantic communications Deep Learning Enabled Semantic Communications explores the synergy between deep learning and semantic communication, particularly in the context of advancing 6G networks. It provides a focused introduction to the subject, systematically covering deep learning enabled semantic communication systems and task-oriented semantic transmission paradigms in wireless communication. The book reviews various aspects of semantic communications, including information theory, multimodal technologies, semantic noise, and semantic sensing. It explores cutting-edge semantic communication architectures, highlighting their advantages over traditional approaches and their potential to drive the future of intelligent information industry. The book also details applications of deep learning-based semantic communication systems across various sources, including text, speech, images, and videos, comprehensively addressing system design, performance optimization, and measurement metrics. The book is divided into eight main parts, which cover foundational knowledge, system design, multimodal and multitask-oriented semantic communication systems, joint semantic sensing and sampling, semantic noise suppression, and generative AI enabled systems. Written by a diverse group of experts in academia and research institutions, Deep Learning Enabled Semantic Communications includes information on: Fundamental knowledge about deep learning and semantic communications, including the history, neural networks, and semantic information theory Compression of multimodal inputs, extraction of global semantic information, and the design of neural networks to boost the capability of handling lengthy speech Incorporation of different sources to extract semantic features and serve diverse intelligent tasks at the receiver Introduction of semantic impairments in communications to uncover how to design robust systems Joint design of data sampling, compression, and coding schemes under the guidance of semantic information Framework of generative semantic communications to detail the principles of incorporating generative models into semantic communications Deep Learning Enabled Semantic Communications is an essential learning resource and reference for graduate and undergraduate students pursuing degrees in wireless communications, signal processing, or deep learning as well as engineers in the telecommunications and IT industries focusing on wireless communication techniques.

This SpringerBrief discusses the applications of spare representation in wireless communications, with a particular focus on the most recent developed compressive sensing (CS) enabled approaches. With the help of sparsity property, sub-Nyquist sampling can be achieved in wideband cognitive radio networks by adopting compressive sensing, which is illustrated in this brief, and it starts with a comprehensive overview of compressive sensing principles. Subsequently, the authors present a complete framework for data-driven compressive spectrum sensing in cognitive radio networks, which guarantees robustness, low-complexity, and security.Particularly, robust compressive spectrum sensing, low-complexity compressive spectrum sensing, and secure compressive sensing based malicious user detection are proposed to address the various issues in wideband cognitive radio networks. Correspondingly, the real-world signals and data collected by experiments carried out during TV white space pilot trial enables data-driven compressive spectrum sensing. The collected data are analysed and used to verify our designs and provide significant insights on the potential of applying compressive sensing to wideband spectrum sensing.This SpringerBrief  provides readers a clear picture on how to exploit the compressive sensing to process wireless signals in wideband cognitive radio networks.  Students, professors, researchers, scientists, practitioners, and engineers working in the fields of compressive sensing in wireless communications will find this SpringerBrief  very useful as a short reference or study guide book.  Industry managers, and government research agency employees also working in the fields of compressive sensing in wireless communications will find this SpringerBrief useful as well.

This SpringerBrief discusses the applications of spare representation in wireless communications, with a particular focus on the most recent developed compressive sensing (CS) enabled approaches. With the help of sparsity property, sub-Nyquist sampling can be achieved in wideband cognitive radio networks by adopting compressive sensing, which is illustrated in this brief, and it starts with a comprehensive overview of compressive sensing principles. Subsequently, the authors present a complete framework for data-driven compressive spectrum sensing in cognitive radio networks, which guarantees robustness, low-complexity, and security.

 Particularly, robust compressive spectrum sensing, low-complexity compressive spectrum sensing, and secure compressive sensing based malicious user detection are proposed to address the various issues in wideband cognitive radio networks. Correspondingly, the real-world signals and data collected by experiments carried out during TV white space pilot trial enables data-driven compressive spectrum sensing. The collected data are analysed and used to verify our designs and provide significant insights on the potential of applying compressive sensing to wideband spectrum sensing.

 This SpringerBrief  provides readers a clear picture on how to exploit the compressive sensing to process wireless signals in wideband cognitive radio networks.  Students, professors, researchers, scientists, practitioners, and engineers working in the fields of compressive sensing in wireless communications will find this SpringerBrief  very useful as a short reference or study guide book.  Industry managers, and government research agency employees also working in the fields of compressive sensing in wireless communications will find this SpringerBrief useful as well.

This book discusses non-orthogonal multiple access (NOMA) and the various issues in NOMA networks, including capability, sustainability, and security. This book starts from the basics and key techniques of NOMA. Subsequently, the authors discuss three critical issues in NOMA networks, including compatibility, sustainability, and security. Particularly, the authors first demonstrate the applications of NOMA in different networks including MIMO-NOMA, NOMA in heterogeneous networks, and NOMA in cognitive radio networks to show the compatibility of NOMA with various networks. Then the wireless powered NOMA networks are presented to address the sustainability issues in NOMA networks to extend the network reliability and lifetime. The security enhanced NOMA networks are discussed for single antenna case and multiple antenna case, respectively. Finally, the most recent developments on artificial intelligence (AI) enabled NOMA networks are discussed and the research challenges on NOMA to support massive number of devices are identified.

This book discusses non-orthogonal multiple access (NOMA) and the various issues in NOMA networks, including capability, sustainability, and security. This book starts from the basics and key techniques of NOMA. Subsequently, the authors discuss three critical issues in NOMA networks, including compatibility, sustainability, and security. Particularly, the authors first demonstrate the applications of NOMA in different networks including MIMO-NOMA, NOMA in heterogeneous networks, and NOMA in cognitive radio networks to show the compatibility of NOMA with various networks. Then the wireless powered NOMA networks are presented to address the sustainability issues in NOMA networks to extend the network reliability and lifetime. The security enhanced NOMA networks are discussed for single antenna case and multiple antenna case, respectively. Finally, the most recent developments on artificial intelligence (AI) enabled NOMA networks are discussed and the research challenges on NOMA to support massive number of devices are identified. 

This book discusses the evolving designs and applications of multimedia content delivery and focuses on computing-based methods.  It offers readers an in-depth understanding of how computational resources at both the source and the destination of the networking continuum can be exploited. This enhances the overall performance of multimedia data networking.This book also presents novel designs and applications focusing on information delivery based on computing. It starts with an overview of the multimedia computational communications as well as spanning topics. The topics range in experience evaluation using electroencephalography, semantic knowledge bases with the next generation of multiple access, end-to-end semantic communication framework and cloud-edge-end intelligent coordination computing.  The authors believe this book offers readers a clear picture of the current state and the next steps in multimedia computational communication networks.Graduate students majoring in the areas of communication networks, computer science and engineering and electrical engineering will find this book useful as a secondary text or reference book. Professionals and researchers working in computational transmission solutions for multimedia communication networks will find this book to be a valuable resource as well.

This book discusses the evolving designs and applications of multimedia content delivery and focuses on computing-based methods.  It offers readers an in-depth understanding of how computational resources at both the source and the destination of the networking continuum can be exploited. This enhances the overall performance of multimedia data networking.This book also presents novel designs and applications focusing on information delivery based on computing. It starts with an overview of the multimedia computational communications as well as spanning topics. The topics range in experience evaluation using electroencephalography, semantic knowledge bases with the next generation of multiple access, end-to-end semantic communication framework and cloud-edge-end intelligent coordination computing.  The authors believe this book offers readers a clear picture of the current state and the next steps in multimedia computational communication networks.Graduate students majoring in the areas of communication networks, computer science and engineering and electrical engineering will find this book useful as a secondary text or reference book. Professionals and researchers working in computational transmission solutions for multimedia communication networks will find this book to be a valuable resource as well.

This textbook provides a comprehensive, step-by-step guide to Semantic Communications, focusing on how deep learning enhances its performance in modern communication systems. The purpose of the book is to offer students, researchers, and engineers a solid understanding of the theoretical principles and practical applications of semantic communication. The text builds from foundational concepts to advanced topics, making it suitable for graduate-level courses and beyond, while also serving as a reference for professionals looking to incorporate semantic communication into real-world systems. By moving beyond conventional data transmission to focus on meaning and intent, this textbook highlights how AI and deep learning are revolutionizing joint source-channel coding, bandwidth optimization, and task-oriented communications. With a strong emphasis on 6G and next-generation networks, this book offers a comprehensive guide to the theoretical foundations and practical applications of semantic communication, making it an essential resource for researchers, students, and professionals shaping the future of AI-driven telecommunications. The book features discussion questions, MATLAB or Python code for experiments and simulations related to semantic communication systems, PowerPoint slides for instructors, and a solutions manual.Combines theoretical principles with practical implementations of semantic communication systems;Addresses the foundational concepts and the hands-on application of deep learning to improve communication efficiency;Features discussion questions, MATLAB code for experiments, PowerPoint slides for instructors, and a solutions manual.