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.

本研究提出了一种基于深度神经网络和迁移学习的语义通信系统，名为 MR_DeepSC，此系统解决了多用户情况下应用语义通信的问题，并在低信噪比条件下实现了最佳性能。