Variational neural inference enhanced text semantic communication system

Recently, deep learning-based semantic communication has garnered widespread attention, with numerous systems designed for transmitting diverse data sources, including text, image, and speech, etc. While efforts have been directed toward improving system performance, many studies have concentrated on enhancing the structure of the encoder and decoder. However, this often overlooks the resulting increase in model complexity, imposing additional storage and computational burdens on smart devices. Furthermore, existing work tends to prioritize explicit semantics, neglecting the potential of implicit semantics. This paper aims to easily and effectively enhance the receiver's decoding capability without modifying the encoder and decoder structures. We propose a novel semantic communication system with variational neural inference for text transmission. Specifically, we introduce a simple but effective variational neural inferer at the receiver to infer the latent semantic information within the received text. This information is then utilized to assist in the decoding process. The simulation results show a significant enhancement in system performance and improved robustness.