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.
翻译:虽然语义通信在大量任务中表现令人满意,但语义噪音和系统稳健性的影响没有得到很好调查。语义通信是语义通信系统中的一种特殊噪音,它指的是预定语义符号和收到符号之间的误导。在本文件中,我们首先提议一个框架,用于强大的端到端语义通信系统,以打击语义噪音。特别是,我们分析语义噪音的原因,并提出产生这种声音的实用方法。为了消除语义噪音的影响,建议进行对抗性培训,将带有语义噪音的样本纳入培训数据集。然后,隐形自动编码器(MAE)被设计成一个强大的语义通信系统结构,其中部分内容被遮盖。为了进一步提高语义通信系统的稳健性,我们首先使用矢量二次偏移-变异自动编码(VQ-VAE)来设计一个离散的代码,由传输器和接收器共享的语义音义噪音样本纳入培训数据集。然后,遮固的自动编码自动编码自动编码的自动编码码码码码器将显示我们拟制式传输系统。