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.

翻译：本文针对带宽受限通信系统中语义信息保持的紧迫挑战，提出一种基于自适应重复编码的新型强化学习框架，实现按维度非均等差错保护。本方法的核心在于构建复合语义失真度量，该度量平衡全局嵌入相似性与实体级保持能力，使强化学习智能体能够以情境感知方式分配保护资源。实验结果表明，与均匀保护方案相比，本方法在1 dB信噪比条件下获得统计显著性提升：chrF分数提高6.8%，实体保持率提升9.3%。本框架的核心创新在于证明：通过智能分配的简单重复编码即可实现细粒度语义保护——这是LDPC或Reed-Solomon等传统编码无法实现的优势。我们的研究结果通过论证编码结构必须与语义粒度相匹配，对传统信道编码范式提出了挑战。该方法特别适用于边缘计算和物联网场景，这些场景中带宽稀缺但语义保真度至关重要，为下一代语义感知网络提供了实用化路径。