Informationen zum Autor Zhijin Qin is an Associate Professor with Tsinghua University, China. She is an Associate Editor for IEEE Transactions on Communications, IEEE Transactions on Cognitive Networking, and IEEE Communications Letters. Dr. Huiqiang Xie, is an Associate Professor at Jinan University, Guangzhou, Guangdong, China. Zhenzi Weng is a Postdoctoral researcher at Imperial College London, UK. Xiaoming Tao is a Professor with the Department of Electronic Engineering at Tsinghua University. She is also a Senior Member of the IEEE. Klappentext 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 the 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. Inhaltsverzeichnis Foreword ix Preface xi Acknowledgments xv Acronyms xvii Notations xxi 1 Introduction 1 1.1 Conventional Communications vs. Semantic Communications 2 1.1.1 Three-Level Communications . . . . . . . . . . . . . 2 1.1.2 History of Semantic Communications . . . . . . . . . 5 1.2 Introducing Deep Learning to Semantic Communications . . . . 6 1.2.1 Deep Learning Basics . . . . . . . . . . . . . . . . . . 6 1.2.2 Deep Learning Enabled Semantic Communications . 17 1.3 Semantic Communications for Further Networks . . . . . . . . . 20 2 Semantic Information Theory 31 2.1 Semantic Entropy . . ...

Autorentext

Zhijin Qin is an Associate Professor with Tsinghua University, China. She is an Associate Editor for IEEE Transactions on Communications, IEEE Transactions on Cognitive Networking, and IEEE Communications Letters. Huiqiang Xie, PhD, is an Associate Professor at Jinan University, Guangzhou, Guangdong, China. Zhenzi Weng is a Postdoctoral researcher at Imperial College London, UK. Xiaoming Tao is a Professor with the Department of Electronic Engineering at Tsinghua University. She is also a Senior Member of the IEEE.

Klappentext

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 the 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.

  Inhalt

  Foreword ix

  Preface xi

  Acknowledgments xv

  Acronyms xvii

  Notations xxi

  1 Introduction 1

  1.1 Conventional Communications vs. Semantic Communications 2

  1.1.1 Three-Level Communications . . . . . . . . . . . . . 2

  1.1.2 History of Semantic Communications . . . . . . . . . 5

  1.2 Introducing Deep Learning to Semantic Communications . . . . 6

  1.2.1 Deep Learning Basics . . . . . . . . . . . . . . . . . . 6

  1.2.2 Deep Learning Enabled Semantic Communications . 17

  1.3 Semantic Communications for Further Networks . . . . . . . . . 20

  2 Semantic Information Theory 31

  2.1 Semantic Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

  2.1.1 Logical Probability-based . . . . . . . . . . . . . . . . 32

  2.1.2 Synonymous mapping-based . . . . . . . . . . . . . . 34

  2.1.3 Fuzzy Theory-based . . . . . . . . . . . . . . . . . . . 37

  2.1.4 Task-based . . . . . . . . . . . . . . . . . . . . . . . . 38

  2.2 Semantic Channel Capacity . . . …