Author Description

<p><b>Comprehensive overview of the principles, theories, and techniques behind deep learning enabled semantic communications</b> <p><i>Deep Learning Enabled Semantic Communications</i> 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. <p>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. <p>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. <p>Written by a diverse group of experts in academia and research institutions, <i>Deep Learning Enabled Semantic Communications </i>includes information on: <ul><li> Fundamental knowledge about deep learning and semantic communications, including the history, neural networks, and semantic information theory</li><li> Compression of multimodal inputs, extraction of global semantic information, and the design of neural networks to boost the capability of handling lengthy speech</li><li> Incorporation of different sources to extract semantic features and serve diverse intelligent tasks at the receiver</li