用强化学习实现语义嵌入的差异化保护，提升低带宽下信息保真度。
Reinforcement-Learned Unequal Error Protection for Quantized Semantic Embeddings
- 通过强化学习动态分配每维语义特征的纠错强度
- 在1 dB信噪比下，字符匹配率提升6.8%，实体保留率提高9.3%
- 适合边缘计算与物联网场景，强调语义粒度的编码新范式
本文针对带宽受限通信系统中保持语义意义的难题，提出一种基于强化学习的新型框架，通过自适应重复编码实现逐维度的不均衡错误保护。核心在于设计了一种复合语义失真度量，兼顾全局嵌入相似性与实体级别保真度，使强化学习智能体能上下文感知地分配保护策略。实验表明，该方法显著优于均匀保护，在1 dB信噪比下，chrF得分提升6.8%，实体保留率改善9.3%。关键创新在于证明：简单但智能分配的重复编码可实现细粒度语义保护，这是传统码如LDPC或Reed-Solomon无法实现的优势。研究挑战了传统信道编码范式，指出编码结构必须与语义粒度对齐。该方法特别适用于带宽稀缺但语义保真至关重要的边缘计算和IoT场景，为下一代语义感知网络提供可行路径。
原文摘要 · Abstract (English)
This paper tackles the pressing challenge of preserving semantic meaning in communication systems constrained by limited bandwidth. We introduce a novel reinforcement learning framework that achieves per-dimension unequal error protection via adaptive repetition coding. Central to our approach is a composite semantic distortion metric that balances global embedding similarity with entity-level preservation, empowering the reinforcement learning agent to allocate protection in a context-aware manner. Experiments show statistically significant gains over uniform protection, achieving 6.8% higher chrF scores and 9.3% better entity preservation at 1 dB SNR. The key innovation of our framework is the demonstration that simple, intelligently allocated repetition coding enables fine-grained semantic protection -- an advantage unattainable with conventional codes such as LDPC or Reed-Solomon. Our findings challenge traditional channel coding paradigms by establishing that code structure must align with semantic granularity. This approach is particularly suited to edge computing and IoT scenarios, where bandwidth is scarce, but semantic fidelity is critical, providing a practical pathway for next-generation semantic-aware networks.
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