Real-Time Cross-Layer Semantic Error Correction Using Language Models and Software-Defined Radio
It addresses reliable network communication by enabling real-time error correction, though it is incremental as it builds on prior work by validating feasibility.
This paper tackled the problem of real-time semantic error correction in networks by implementing Cross-Layer Semantic Error Correction on a live Software-Defined Radio testbed, achieving significant performance improvements over single-source methods.
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.