Separate Source-Channel Coding (SSCC) remains attractive for text transmission due to its modularity and compatibility with mature entropy coders and powerful channel codes. However, SSCC often suffers from a pronounced cliff effect in low Signal-to-Noise Ratio (SNR) regimes, where residual bit errors after channel decoding can catastrophically break lossless source decoding, especially for Arithmetic Coding (AC) driven by Large Language Models (LLMs). This paper proposes a receiver-side In-Context Decoding (ICD) framework that enhances SSCC robustness without modifying the transmitter. ICD leverages an Error Correction Code Transformer (ECCT) to obtain bit-wise reliability for the decoded information bits. Based on the context-consistent bitstream, ICD constructs a confidence-ranked candidate pool via reliability-guided bit flipping, samples a compact yet diverse subset of candidates, and applies an LLM-based arithmetic decoder to obtain both reconstructions and sequence-level log-likelihoods. A reliability-likelihood fusion rule then selects the final output. We further provide theoretical guarantees on the stability and convergence of the proposed sampling procedure. Extensive experiments over Additive White Gaussian Noise (AWGN) and Rayleigh fading channels demonstrate consistent gains compared with conventional SSCC baselines and representative Joint Source-Channel Coding (JSCC) schemes.

翻译：分离式源信道编码（SSCC）因其模块化特性以及与成熟熵编码器和强大信道编码器的兼容性，在文本传输领域仍具有显著优势。然而，SSCC在低信噪比（SNR）条件下常面临严重的“悬崖效应”：信道解码后的残留比特误差可能灾难性地破坏无损源解码过程，尤其对于由大语言模型（LLMs）驱动的算术编码（AC）而言。本文提出一种接收端上下文解码（ICD）框架，在不改变发射机结构的前提下增强SSCC的鲁棒性。该框架利用纠错码变换器（ECCT）获取解码信息比特的逐比特可靠性度量。基于上下文一致的比特流，ICD通过可靠性引导的比特翻转构建置信度排序的候选池，采样紧凑而多样化的候选子集，并应用基于LLM的算术解码器同时获取重构序列和序列级对数似然。最终通过可靠性-似然融合规则选定输出结果。我们进一步为所提采样过程的稳定性与收敛性提供了理论保证。在加性高斯白噪声（AWGN）与瑞利衰落信道上的大量实验表明，相较于传统SSCC基线方案和典型联合源信道编码（JSCC）方案，本方法均能获得持续的性能增益。