Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.

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In the low signal-to-noise ratio region, a large number of bit errors occur, and it may exceed the channel error correction capability of the receiver. Traditional communication system may use the technology of automatic repeat-request to deal with this problem, which is time consuming and a waste of resources. To enhance the reliability of the communication system, we investigate reasoning and decoding at the semantic level instead of the grammar level. In particular, we propose a semantic communication model for text transmission, assisting the communication system to be more robust in terrible channel environments. Based on the traditional communication system, the language model BERT, part of speech tagging, and prior information concerning bit-flipping are introduced to enhance the semantic reasoning ability of the transceiver. Furthermore, this paper analyzes the effects of the sub-strategies on the performances of the improved communication model, such as the existence of a candidate set and language model. The numerical results show the effectiveness of our model in terms of improving the semantic accuracy measured by BLUE, the METEOR score, and the similarity score based on BERT between transmitted messages and recovered messages.

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