Knowledge Enhanced Semantic Communication Receiver

TL;DR

This paper introduces a knowledge-enhanced semantic communication receiver that actively utilizes a knowledge base for improved semantic decoding, demonstrating superior performance on the WebNLG dataset.

Contribution

It proposes a novel transformer-based knowledge extractor for the receiver to improve semantic decoding using prior knowledge, without altering the transmitter structure.

Findings

Superior decoding performance on WebNLG dataset

Effective utilization of prior knowledge for semantic reasoning

Enhanced robustness to noisy signals

Abstract

In recent years, with the rapid development of deep learning and natural language processing technologies, semantic communication has become a topic of great interest in the field of communication. Although existing deep learning-based semantic communication approaches have shown many advantages, they still do not make sufficient use of prior knowledge. Moreover, most existing semantic communication methods focus on the semantic encoding at the transmitter side, while we believe that the semantic decoding capability of the receiver should also be concerned. In this paper, we propose a knowledge enhanced semantic communication framework in which the receiver can more actively utilize the facts in the knowledge base for semantic reasoning and decoding, on the basis of only affecting the parameters rather than the structure of the neural networks at the transmitter side. Specifically, we design a transformer-based knowledge extractor to find relevant factual triples for the received noisy signal. Extensive simulation results on the WebNLG dataset demonstrate that the proposed receiver yields superior performance on top of the knowledge graph enhanced decoding.