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With the advent of the 6G era, the concept of semantic communication hasattracted increasing attention. Compared with conventional communicationsystems, semantic communication systems are not only affected by physical noiseexisting in the wireless communication environment, e.g., additional whiteGaussian noise, but also by semantic noise due to the source and the nature ofdeep learning-based systems. In this paper, we elaborate on the mechanism ofsemantic noise. In particular, we categorize semantic noise into twocategories: literal semantic noise and adversarial semantic noise. The formeris caused by written errors or expression ambiguity, while the latter is causedby perturbations or attacks added to the embedding layer via the semanticchannel. To prevent semantic noise from influencing semantic communicationsystems, we present a robust deep learning enabled semantic communicationsystem (R-DeepSC) that leverages a calibrated self-attention mechanism andadversarial training to tackle semantic noise. Compared with baseline modelsthat only consider physical noise for text transmission, the proposed R-DeepSCachieves remarkable performance in dealing with semantic noise under differentsignal-to-noise ratios.

A Robust Deep Learning Enabled Semantic Communication System for Text