基于伴随式验证语义先验的大语言模型辅助低密度奇偶校验（LDPC）译码
LLM-Assisted LDPC Decoding via Syndrome-Verified Semantic Priors
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中文总结 AI 辅助
该研究提出LLM辅助LDPC译码框架，通过伴随式验证语义先验减少误码率，在2.0 dB时误码率降低73%，且注入精度保持在0.88以上，提升了物理层可靠性。
中文摘要 AI 辅助
语义通信利用有效载荷的含义，这是比特级处理所丢弃的内容。当信道译码在自然语言有效载荷上失败时，错误会表现为恢复文本中的损坏字符。大语言模型（LLM）可从语义上下文推断出预期字符，但也可能产生不正确的修正。直接应用这些修正会在LLM错误修改字符时引入新的比特错误。本文提出一种用于低密度奇偶校验（LDPC）码的LLM辅助译码框架。译码器不直接信任LLM预测，而是联合校验奇偶校验约束来评估修改后的字符，仅接受经验证的修正作为语义先验。这些先验作为软更新注入到信道对数似然比中，在不修改译码器的情况下保留原始信道证据。后续的置信传播过程将注入的证据分布到各个校验节点，不仅恢复注入的比特，还能纠正LLM未能修正的剩余错误。仿真结果显示，在2.0 dB时，该方法相比传统译码器可降低73%的误码率，而在相同预算下将传统译码器的迭代次数翻倍仅能降低21%。尽管LLM预测存在误差，但该验证机制使注入精度保持在0.88以上，证明语义知识可可靠转化为物理层可靠性增益。
英文摘要
Semantic communication exploits the meaning of the payload, which bit-level processing discards. When channel decoding fails on a natural language payload, the errors appear as corrupted characters in the recovered text. A large language model (LLM) infers the intended characters from the semantic context, but it can also produce incorrect corrections. Applying them directly introduces new bit errors when the LLM modifies characters incorrectly. In this paper, we propose an LLM-assisted decoding framework for low-density parity-check (LDPC) codes. Rather than trusting LLM predictions, the decoder evaluates the modified characters jointly against the parity-check constraints and admits only the accepted corrections as verified semantic priors. These priors are injected as soft updates to the channel log-likelihood ratios, preserving the original channel evidence without modifying the decoder. A subsequent belief propagation pass distributes the injected evidence across the check nodes, recovering not only the injected bits but also the residual errors that the LLM fails to correct. Simulations demonstrate a 73% bit error rate reduction over a conventional decoder at 2.0 dB, whereas doubling its iterations to the same budget yields only 21%. The verification maintains an injection precision above 0.88 despite inaccurate LLM predictions, demonstrating that semantic knowledge can be reliably translated into physical-layer reliability gains.
