Traditional wireless communications rely solely on bit-level channel coding for error correction, without exploiting the inherent linguistic structure of the data source. This paper proposes a large language model (LLM) Viterbi decoder that integrates LLM priors into the Viterbi decoding for text transmission over AWGN channels. The proposed decoder maintains multiple candidate paths during the Viterbi decoding and periodically evaluates path reliabilities using a fine-tuned Byte-level T5 (ByT5) language model. By combining channel reliability metrics with semantic probability from the LLM, it outputs the path that maximizes the joint likelihood of channel observations and linguistic coherence. Simulations show that our decoder achieves significant performance gains over conventional Viterbi decoding in terms of both block error rate (BLER) and semantic similarity. For convolutional codes with constraint length 3, it achieves approximately 1.5 dB more coding gain in BLER, with over 50% improvements in semantic similarity. The framework can extend to other structured data sources beyond text.

翻译：传统无线通信依赖比特级信道编码进行纠错，未能利用数据源固有的语言结构。本文提出一种大语言模型维特比解码器，将大语言模型先验信息集成到维特比解码中，用于加性高斯白噪声信道下的文本传输。该解码器在维特比解码过程中维护多条候选路径，并利用微调后的字节级T5语言模型周期性地评估路径可靠度。通过将信道可靠性度量与大语言模型的语义概率相结合，最终输出使信道观测值与语言连贯性联合似然最大化的路径。仿真表明，在误块率和语义相似度方面，该解码器均显著优于传统维特比解码。对于约束长度为3的卷积码，其在误块率上获得约1.5分贝的额外编码增益，语义相似度提升超过50%。该框架可推广至文本以外的其他结构化数据源。