让语义通信更抗干扰，直接适配数字信道。
Channel-Aware Vector Quantization for Robust Semantic Communication on Discrete Channels
- 根据信道状态优化量化码本，提升鲁棒性。
- 在多种调制方式下重建质量更优，突破数字悬崖效应。
- 适合追求高效可靠数字语义传输的工程师。
基于深度学习的语义通信主要依赖模拟或半数字传输，难以兼容现代数字通信架构。近期研究采用向量量化（VQ）实现离散语义传输，但现有方法在码本优化中忽略信道状态信息，导致鲁棒性不足。为此，本文提出一种在联合源信道编码（JSCC）框架下的通道感知向量量化（CAVQ）算法，称为VQJSCC，基于离散无记忆信道构建。该框架将语义特征离散化并直接映射到调制星座符号，通过将信道转移概率融入量化过程，使易混淆符号与语义相似的码字对齐。进一步引入多码本对齐机制，通过将传输流分解为多个独立优化的子信道，缓解码本顺序与调制顺序不匹配的问题。实验表明，VQJSCC有效缓解了数字悬崖效应，在多种调制方案下均实现更优重建质量，且在鲁棒性和效率上超越当前最先进的数字语义通信基线。
原文摘要 · Abstract (English)
Deep learning-based semantic communication has largely relied on analog or semi-digital transmission, which limits compatibility with modern digital communication infrastructures. Recent studies have employed vector quantization (VQ) to enable discrete semantic transmission, yet existing methods neglect channel state information during codebook optimization, leading to suboptimal robustness. To bridge this gap, we propose a channel-aware vector quantization (CAVQ) algorithm within a joint source-channel coding (JSCC) framework, termed VQJSCC, established on a discrete memoryless channel. In this framework, semantic features are discretized and directly mapped to modulation constellation symbols, while CAVQ integrates channel transition probabilities into the quantization process, aligning easily confused symbols with semantically similar codewords. A multi-codebook alignment mechanism is further introduced to handle mismatches between codebook order and modulation order by decomposing the transmission stream into multiple independently optimized subchannels. Experimental results demonstrate that VQJSCC effectively mitigates the digital cliff effect, achieves superior reconstruction quality across various modulation schemes, and outperforms state-of-the-art digital semantic communication baselines in both robustness and efficiency.
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