As Language Models (LMs) advance, Semantic Error Correction (SEC) has emerged as a promising approach for reliable network designs. Yet existing methods prioritize intent over accuracy, falling short of verbatim recovery. Our recent work, Cross-Layer SEC (CL-SEC), addressed this by fusing physical-layer Log-Likelihood Ratios (LLRs) with semantic context, but its real-time feasibility remained unvalidated. This paper demonstrates CL-SEC on a live Software-Defined Radio (SDR) testbed, resolving implementation barriers with: 1) an SDR middleware enabling real-time LLR extraction from FPGA hardware, and 2) a generalized inference interface supporting modern encoder-decoder LMs. Real-world experiments confirm that the cross-layer fusion significantly outperforms either source alone.

翻译：随着语言模型的发展，语义纠错作为一种可靠的网络设计方法崭露头角。然而，现有方法倾向于强调意图而非准确性，无法实现逐字还原。我们近期提出的跨层语义纠错方法通过融合物理层的对数似然比与语义上下文解决了这一问题，但其实时可行性尚未得到验证。本文在实时软件无线电测试平台上成功部署了跨层语义纠错，通过以下方式攻克实现障碍：1) 一种支持从FPGA硬件实时提取对数似然比的软件无线电中间件，2) 一个兼容现代编码器-解码器语言模型的泛化推理接口。实际实验证实，跨层融合的性能显著优于任一单一信息来源。