论文标题
一对多的语义通信系统：设计，实施，绩效评估
One-to-Many Semantic Communication Systems: Design, Implementation, Performance Evaluation
论文作者
论文摘要
6G时代的语义沟通被认为是一个有希望的交流范式，可以突破传统交流的瓶颈。但是，其在多用户方案（尤其是广播案例）上的应用程序仍然不足。为了有效利用语义沟通启用的好处，在本文中，我们提出了一个一对一的语义通信系统。具体而言，我们建议使用一个启用的深神经网络（DNN），称为MR \ _DeepSc。通过为不同用户提供语义功能，基于预训练的模型即Distilbert的语义识别器是为了区分不同用户的。此外，采用转移学习来加快新接收器网络的培训。仿真结果表明，在不同的通道条件下，尤其是在低信噪比（SNR）方面，提出的MR \ _DEEPSC可以在BLEU评分方面取得最佳性能。
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.
