Deep Learning Enabled Semantic Communications

Published by Wiley-IEEE Press, Deep Learning Enabled Semantic Communications (1st Edition) delivers an authoritative reference examining task-oriented semantic transmission paradigms in wireless communication. Authors Zhijin Qin, Huiqiang Xie, Zhenzi Weng, and Xiaoming Tao systematically explore how neural network architectures advance 6G networks, grounding their technical analysis in semantic information theory and fundamental system design.

The volume details deep learning implementations across text, speech, image, and video transmission while evaluating system design, performance optimization, and measurement metrics. Dedicated coverage addresses the compression of multimodal inputs, the extraction of global semantic information, and specialized neural network design engineered to improve the transmission of lengthy speech streams.

Structured into eight main functional sections that range from foundational knowledge to joint semantic sensing and generative AI integration, this text provides targeted guidance for undergraduate and graduate students pursuing degrees in wireless communications, signal processing, or deep learning.