Deep Learning-Empowered Semantic Communication Systems with a Shared
  Knowledge Base
Peng Yi Yang Cao Xin Kang Ying-Chang Liang
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.