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.