用语义重传提升文本通信可靠性，不改协议栈就能增强鲁棒性。
Generative Semantic HARQ: Latent-Space Text Retransmission and Combining
- 在传统协议上叠加轻量级语义编码器，实现多轮重传中隐空间的增量知识传递。
- 混合语义失真下，加权平均或类MRC合并策略配合自一致性触发，性能最优。
- 适合关注语义通信可靠性的研究者和低码率场景下的系统设计者。
语义通信旨在传递意义而非原始比特，但语义层面的可靠性仍是未解难题。本文提出一种面向文本通信的语义级混合自动重传请求（HARQ）框架，其中基于Transformer-变分自编码器（VAE）的编解码器作为轻量级叠加层运行于传统协议栈之上。随机编码器在多次重传中生成多样化的隐空间表示，无需额外协议设计即可提供增量知识（IK）。接收端采用软质量估计算法触发重传，并使用质量感知合并器在一致的隐空间内融合接收到的隐向量。我们在混合语义失真（包含系统性偏差与加性噪声）条件下，系统评估了六种语义质量指标和四种软合并策略。结果表明，在加权平均或类最大比合并（MRC-Inspired）合并策略与基于自一致性检测的HARQ触发机制结合时，性能最佳。
原文摘要 · Abstract (English)
Semantic communication conveys meaning rather than raw bits, but reliability at the semantic level remains an open challenge. We propose a semantic-level hybrid automatic repeat request (HARQ) framework for text communication, in which a Transformer-variational autoencoder (VAE) codec operates as a lightweight overlay on the conventional protocol stack. The stochastic encoder inherently generates diverse latent representations across retransmissions-providing incremental knowledge (IK) from a single model without dedicated protocol design. On the receiver side, a soft quality estimator triggers retransmissions and a quality-aware combiner merges the received latent vectors within a consistent latent space. We systematically benchmark six semantic quality metrics and four soft combining strategies under hybrid semantic distortion that mixes systematic bias with additive noise. The results suggest combining Weighted-Average or MRC-Inspired combining with self-consistency-based HARQ triggering for the best performance.
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