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

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 e...

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Bibliographic Details
Published in:IEEE communications letters 2022-12, Vol.26 (12), p.2959-2963
Main Authors: Hu, Han, Zhu, Xingwu, Zhou, Fuhui, Wu, Wei, Hu, Rose Qingyang, Zhu, Hongbo
Format: Article
Language:English
Subjects:
Artificial neural networks
Communication systems
Communications systems
Decoding
Deep learning
Machine learning
multi-user communications
Performance evaluation
Receivers
semantic communications
Semantics
Signal to noise ratio
Symbols
Training
Transmitters
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Summary: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.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2022.3203984