Robust Semantic Communications With Masked VQ-VAE Enabled Codebook

Although semantic communications have exhibited satisfactory performance on a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise refers to the misleading between the intended semantic symbols and received ones, thus causes the failure of tasks. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. In particular, we analyze sample-dependent and sample-independent semantic noise. To combat the semantic noise, the adversarial training with weight perturbation is developed to incorporate the samples with semantic noise in the training dataset. Then, we…

• NN
  National Natural Science Foundation of China

  Awards: U22A2004, 61971376, 62293484, 61925105, 61831004
• TU
  Tsinghua University
• FR
  Fundamental Research Funds for the Central Universities

  Award: 226-2022-00195