Joint Coding-Modulation for Digital Semantic Communications via Variational Autoencoder

• Published in: IEEE transactions on communications Vol. 72; no. 9; pp. 5626 - 5640
• Main Authors: Bo, Yufei, Duan, Yiheng, Shao, Shuo, Tao, Meixia
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Coding; Communication; Communications systems; Decoding; Digital imaging; Digital modulation; Information theory; Modulation; mutual information; Neural networks; Probabilistic logic; Receivers; Semantic communications; Semantics; Symbols; Transition probabilities; variational autoencoder
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2024.3386577

Semantic communications have emerged as a new paradigm for improving communication efficiency by transmitting the semantic information of a source message that is most relevant to a desired task at the receiver. Most existing approaches typically utilize neural networks (NNs) to design end-to-end semantic communication systems, where NN-based semantic encoders output continuously distributed signals to be sent directly to the channel in an analog fashion. In this work, we propose a joint coding-modulation (JCM) framework for digital semantic communications by using variational autoencoder (VAE). Our approach learns the transition probability from source data to discrete constellation symbols, thereby avoiding the non-differentiability problem of digital modulation. Meanwhile, by jointly designing the coding and modulation process together, we can match the obtained modulation strategy with the operating channel condition. We also derive a matching loss function with information-theoretic meaning for end-to-end training. Experiments on image semantic communication validate the superiority of our proposed JCM framework over the state-of-the-art quantization-based digital semantic coding-modulation methods across a wide range of channel conditions, transmission rates, and modulation orders. Furthermore, its performance gap to analog semantic communication reduces as the modulation order increases while enjoying the hardware implementation convenience.