Deep Learning Enabled Semantic Communications - Hardcover

Qin, Zhijin; Xie, Huiqiang; Weng, Zhenzi; Tao, Xiaoming

Hardcover

ISBN 10: 1394306237 ISBN 13: 9781394306237

Publisher: Wiley-IEEE Press, 2026

Synopsis

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.

About this title

About the Author

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.

From the Back Cover

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.

Edition details

• Publisher: Wiley-IEEE Press
• Publication date: 2026
• Language: English
• ISBN 10: 1394306237
• ISBN 13: 9781394306237
• Binding: Hardcover
• Edition number: 1
• Number of pages: 176