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:虽然语义通信在大量任务方面表现令人满意,但语义噪音和系统坚固度的影响没有很好地调查。语义噪音是指预定语义符号和收到符号之间的误导,从而造成任务的失败。在本文件中,我们首先提出一个强大的端到端语语义通信系统框架,以对抗语义噪音。特别是,我们分析依赖样本和依赖样本的语义噪音。为打击语义噪音,开发了带有重度的对抗性培训,以便将带有语义噪音的样本纳入培训数据集。然后,我们提议掩盖一部分输入,经常出现语义噪音,并设计一个与噪音有关的隐蔽矢量分解-变异性通信系统(VQ-VAE)框架。我们使用一个由发报机和接收机共享的离散代码,用于加密特征代表。为进一步改进系统,我们开发了一个具有特征重要性的模块(FIM),以抑制与语义性音义噪音噪音噪音噪音噪声噪声噪声调的样本,从而在与任务上大幅降低与任务变异性变异特性。在与任务代码上的拟议Simunttraveltaltal lactions