Version 1
: Received: 17 October 2023 / Approved: 18 October 2023 / Online: 19 October 2023 (11:49:20 CEST)
Version 2
: Received: 29 January 2024 / Approved: 29 January 2024 / Online: 30 January 2024 (03:49:44 CET)
Xin, G.; Fan, P.; Letaief, K.B. Semantic Communication: A Survey of Its Theoretical Development. Entropy 2024, 26, 102, doi:10.3390/e26020102.
Xin, G.; Fan, P.; Letaief, K.B. Semantic Communication: A Survey of Its Theoretical Development. Entropy 2024, 26, 102, doi:10.3390/e26020102.
Xin, G.; Fan, P.; Letaief, K.B. Semantic Communication: A Survey of Its Theoretical Development. Entropy 2024, 26, 102, doi:10.3390/e26020102.
Xin, G.; Fan, P.; Letaief, K.B. Semantic Communication: A Survey of Its Theoretical Development. Entropy 2024, 26, 102, doi:10.3390/e26020102.
Abstract
In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important yet still unresolved issues. In general, the mathematical theory of semantic communication and the mathematical representation of semantics are referred to as semantic information theory. In this paper, we introduce the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate-distortion, and semantic channel capacity. Additionally, we analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory.
Keywords
Semantic information theory; semantic communication; semantic distortion; 6G; goal-oriented communications; joint source-channel coding; deep learning; information bottleneck
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
