Abstract:

Semantic communication provides a new research perspective for communication system optimization and performance improvement.However,current research on semantic communication ignores the impact of communication overhead and does not consider the relationship between semantic communication performance and communication overhead,resulting in difficulty in improving semantic communication performance when the bandwidth resource is limited.Therefore,an information bottleneck based semantic communication method for text sources is proposed.First,the Transformer model is used for semantic and channel joint encoding and decoding,and a feature selection module is designed to identify and delete redundant information,and then an end-to-end semantic communication model is constructed in the method;Second,considering the tradeoff between semantic communication performance and communication cost,a loss function is designed based on the information bottleneck theory to ensure the semantic communication performance,reduce the communication cost,and complete the training and optimization of the semantic communication model.Experimental results show that on the proceedings of the European Parliament,compared with the baseline model,the proposed method can reduce communication overhead by 20%~30% while ensuring communication performance.Under the same bandwidth conditions,the BLEU score of this method can be increased by 5%.Experimental results prove that the proposed method can effectively reduce the semantic communication overhead,thereby improving semantic communication performance when the bandwidth resource is limited.

Key words: semantic communication, communication systems, deep learning, transformer, feature selection module, information bottleneck theory