  Although the semantic communications have exhibited satisfactory performance
in a large number of tasks, the impact of semantic noise and the robustness of
the systems have not been well investigated. Semantic noise is a particular
kind of noise in semantic communication systems, which refers to the misleading
between the intended semantic symbols and received ones. In this paper, we
first propose a framework for the robust end-to-end semantic communication
systems to combat the semantic noise. Particularly, we analyze the causes of
semantic noise and propose a practical method to generate it. To remove the
effect of semantic noise, adversarial training is proposed to incorporate the
samples with semantic noise in the training dataset. Then, the masked
autoencoder (MAE) is designed as the architecture of a robust semantic
communication system, where a portion of the input is masked. To further
improve the robustness of semantic communication systems, we firstly employ the
vector quantization-variational autoencoder (VQ-VAE) to design a discrete
codebook shared by the transmitter and the receiver for encoded feature
representation. Thus, the transmitter simply needs to transmit the indices of
these features in the codebook. Simulation results show that our proposed
method significantly improves the robustness of semantic communication systems
against semantic noise with significant reduction on the transmission overhead.
