面向未来通信的、基于基础模型的语义差错控制编码
Semantic Error Control Coding with Foundation Models for Future Communications
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中文总结 AI 辅助
本文提出基于基础模型的语义差错控制编码（SECC）框架，将信源语义结构融入编解码，在AWGN信道上较传统解码获数分贝编码增益，错误率低于正态近似界，同时指出相关开放挑战。
中文摘要 AI 辅助
传统信道解码通常将所有信息序列视为等概率，主要依赖信道观测和码结构，未利用源数据中的统计或语义结构。尽管信源压缩旨在去除冗余，但实际信源编码可能残留大量传统信道解码器未利用的结构。现代多模态信源（包括文本、语音和图像）具有丰富的统计和语义依赖关系，基础模型可学习并利用这些关系来改进信道解码。本文介绍语义差错控制编码（SECC），其通过基础模型将信源的语义结构无缝集成到编码和解码过程中。由模型对信源内容的先验概率表示的语义信源先验，在编码器处将码冗余导向语义重要内容，在解码器处改进可靠性估计、候选搜索及错误检测与纠正。信道码保留其代数结构，其约束确保基础模型的语义建议符合该结构。我们描述SECC框架，将其设计方法分为三类，并在文本信源上演示代表性方案。这些方案在AWGN信道上相比传统解码展示了数分贝的编码增益，且达到低于正态近似界的错误率。最后，我们指出若干开放挑战。
英文摘要
Classical channel decoding typically treats all information sequences as equally likely and relies primarily on the channel observations and code structure, without exploiting statistical or semantic structure in the source data. Although source compression is designed to remove redundancy, practical source coding can leave substantial residual structure that conventional channel decoders do not exploit. Modern multimodal data sources, including text, speech, and images, exhibit rich statistical and semantic dependencies that foundation models can learn and exploit to improve channel decoding. This article introduces semantic error control coding (SECC), which seamlessly integrates the semantic structure of the source into encoding and decoding through a foundation model. The semantic source prior, represented by the model's a priori probability of the source content, directs code redundancy toward semantically important content at the encoder, and improves reliability estimation, candidate search, and error detection/correction at the decoder. The channel code keeps its algebraic structure, and its constraints ensure that the semantic suggestions from the foundation model comply with this structure. We describe the SECC framework, classify its design methods into three approaches, and demonstrate representative schemes on text sources. The demonstrated schemes show several decibels of coding gain over conventional decoding on AWGN channels, and reach error rates below the normal approximation bound. Finally, we identify several open challenges.
