知识增强的语义通信接收端设计

Design of knowledge enhanced semantic communication receiver

针对现有的语义通信系统对先验知识利用不够充分、接收端解码能力有限的问题,提出了一个知识增强的语义通信框架。在这个框架中,接收端可以利用知识库中的先验知识进行语义推理和解码,同时不需要对发送端的神经网络结构进行额外的修改。具体而言,在语义接收端的基础上,设计了一个基于Transformer的知识提取器来为接收到的含噪信号寻找语义相关的知识三元组,以用于语义解码。在WebNLG数据集上的仿真结果表明,所提框架在知识图谱增强解码的基础上产生了明显的性能提升。

To address the problem that existing semantic communication do not make sufficient use of prior knowledge and have limited decoding capability at the receiver side,a knowledge enhanced semantic communication framework was proposed,in which the receiver could more actively utilize the prior knowledge in the knowledge base for semantic reasoning and decoding,without extra modifications to the neural network structure of the transmitter.Specifically,a transformer-based knowledge extractor was designed to find relevant factual triples for the received noisy signal.Extensive simulation results on the WebNLG dataset demonstrate that the proposed framework has significantly improved performance on the basis of knowledge graph enhanced decoding.