Deep learning-empowered semantic communication is regarded as a promising candidate for future 6G networks. Although existing semantic communication systems have achieved superior performance compared to traditional methods, the end-to-end architecture adopted by most semantic communication systems is regarded as a black box, leading to the lack of explainability. To tackle this issue, in this paper, a novel semantic communication system with a shared knowledge base is proposed for text transmissions. Specifically, a textual knowledge base constructed by inherently readable sentences is introduced into our system. With the aid of the shared knowledge base, the proposed system integrates the message and corresponding knowledge from the shared knowledge base to obtain the residual information, which enables the system to transmit fewer symbols without semantic performance degradation. In order to make the proposed system more reliable, the semantic self-information and the source entropy are mathematically defined based on the knowledge base. Furthermore, the knowledge base construction algorithm is developed based on a similarity-comparison method, in which a pre-configured threshold can be leveraged to control the size of the knowledge base. Moreover, the simulation results have demonstrated that the proposed approach outperforms existing baseline methods in terms of transmitted data size and sentence similarity.

翻译：深度学习赋能的语义通信被视为未来6G网络中的一项有前景的技术。尽管现有语义通信系统相比传统方法取得了更优越的性能，但大多数语义通信系统采用的端到端架构被视为一个黑箱，导致缺乏可解释性。为解决这一问题，本文提出了一种基于共享知识库的新型语义通信系统，用于文本传输。具体而言，系统中引入了一个由可读性语句构建的文本知识库。借助共享知识库，所提系统将消息与知识库中的对应知识进行整合以获取残差信息，从而在语义性能不下降的情况下减少传输符号数。为提升系统的可靠性，本文基于知识库数学定义了语义自信息和信源熵。此外，基于相似度比较方法开发了一种知识库构建算法，其中可利用预设阈值控制知识库的规模。仿真结果表明，所提方法在传输数据量和语句相似度方面均优于现有基准方法。