Although semantic communications have exhibited satisfactory performance for 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 cause 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 propose to mask a portion of the input, where the semantic noise appears frequently, and design the masked vector quantized-variational autoencoder (VQ-VAE) with the noise-related masking strategy. We use a discrete codebook shared by the transmitter and the receiver for encoded feature representation. To further improve the system robustness, we develop a feature importance module (FIM) to suppress the noise-related and task-unrelated features. Thus, the transmitter simply needs to transmit the indices of these important task-related features in the codebook. Simulation results show that the proposed method can be applied in many downstream tasks and significantly improve the robustness against semantic noise with remarkable reduction on the transmission overhead.

翻译：尽管语义通信已在大量任务中展现出令人满意的性能，但语义噪声的影响及系统的鲁棒性尚未得到充分研究。语义噪声指预期语义符号与接收符号之间的误导，从而导致任务失败。本文首先提出一种鲁棒端到端语义通信系统框架以对抗语义噪声。具体而言，我们分析了样本相关性与样本独立性语义噪声。为对抗语义噪声，我们开发了带权重扰动的对抗训练方法，将含语义噪声的样本纳入训练数据集。进而提出对语义噪声频繁出现的输入部分进行掩蔽，并设计基于噪声相关掩蔽策略的掩蔽向量量化变分自编码器（VQ-VAE）。我们采用发射端与接收端共享的离散码本进行编码特征表示。为提升系统鲁棒性，我们开发了特征重要性模块（FIM）以抑制噪声相关及任务无关特征。因此发射端仅需传输码本中这些重要任务相关特征的索引。仿真结果表明，所提方法可应用于多种下游任务，在显著降低传输开销的同时，大幅提升对语义噪声的鲁棒性。