One-to-Many Semantic Communication Systems: Design, Implementation, Performance Evaluation

TL;DR

This paper introduces MR_DeepSC, a deep neural network-based semantic communication system for multi-user broadcasting in 6G, demonstrating superior performance especially in low SNR conditions through semantic feature utilization and transfer learning.

Contribution

It proposes a novel one-to-many semantic communication system using deep learning and transfer learning, addressing multi-user broadcasting challenges in 6G networks.

Findings

MR_DeepSC outperforms benchmarks in BLEU score across various channel conditions.

The system performs particularly well in low SNR regimes.

Semantic features enable effective multi-user differentiation.

Abstract

Semantic communication in the 6G era has been deemed a promising communication paradigm to break through the bottleneck of traditional communications. However, its applications for the multi-user scenario, especially the broadcasting case, remain under-explored. To effectively exploit the benefits enabled by semantic communication, in this paper, we propose a one-to-many semantic communication system. Specifically, we propose a deep neural network (DNN) enabled semantic communication system called MR\_DeepSC. By leveraging semantic features for different users, a semantic recognizer based on the pre-trained model, i.e., DistilBERT, is built to distinguish different users. Furthermore, the transfer learning is adopted to speed up the training of new receiver networks. Simulation results demonstrate that the proposed MR\_DeepSC can achieve the best performance in terms of BLEU score than the other benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) regime.