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

• URL: http://arxiv.org/abs/2209.09425v1
• Date: Tue, 20 Sep 2022 02:48:34 GMT
• Title: One-to-Many Semantic Communication Systems: Design, Implementation, Performance Evaluation
• Authors: Han Hu, Xingwu Zhu, Fuhui Zhou, Wei Wu, Rose Qingyang Hu, and Hongbo Zhu
• Abstract summary: We propose a one-to-many semantic communication system called MR_DeepSC. By leveraging semantic features for different users, a semantic recognizer is built to distinguish different users. The proposed MR_DeepSC can achieve the best performance in terms of BLEU score.
• Score: 35.21413988605204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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