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.

翻译：基于深度学习的语义通信在很大程度上依赖于模拟或半数字传输，这限制了与现代数字通信基础设施的兼容性。最近的研究采用向量量化（VQ）来实现离散语义传输，然而现有方法在码本优化过程中忽略了信道状态信息，导致鲁棒性欠佳。为了弥补这一差距，我们在建立在离散无记忆信道上的联合信源信道编码（JSCC）框架内，提出了一种信道感知向量量化（CAVQ）算法，该框架称为VQJSCC。在此框架中，语义特征被离散化并直接映射到调制星座符号，而CAVQ将信道转移概率集成到量化过程中，将容易混淆的符号与语义相似的码字对齐。进一步引入了多码本对齐机制，通过将传输流分解为多个独立优化的子信道，来处理码本顺序与调制顺序之间的不匹配问题。实验结果表明，VQJSCC有效缓解了数字悬崖效应，在各种调制方案下实现了卓越的重建质量，并且在鲁棒性和效率方面均优于最先进的数字语义通信基线。