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Score Based Error Correcting Code Decoder

arXiv 2605.28358 ↗First posted 27 May 2026cs.LG cs.AI cs.ITcited by 0

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Abstract (arXiv)

Error-correcting codes enable reliable communication, yet practical soft decoding remains challenging across code families and block lengths. We propose SB-ECC, a score-based decoder that casts decoding as continuous-time denoising. A neural denoiser defines a probability-flow ordinary differential equation (ODE) that iteratively updates the noisy channel observation toward a valid codeword, guided by parity constraints. The model is trained across noise levels without time/SNR conditioning, enabling inference without SNR estimation and supporting a direct latency accuracy trade off controlled by the ODE solver budget. We use the raw signed channel observation as input for learning a continuous denoising field. Across 42 code/SNR settings, SB-ECC achieves the best BER in 39/42 entries, with an average SNR gain of 0.17dB and a maximum gain of 0.46dB over the strongest competing baseline, we showed that swapping the solver from Euler to DPM preserves -ln(BER) while reducing end-to-end decoding time by 8.86% on average (up to 12.82%).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 44 of its 66 references that are in the map. First posted on arXiv 27 May 2026.

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References · 44

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32 more
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  20. 2024CrossMPT: Cross-attention Message-Passing Transformer for Error Correcting Codescited by 20
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  27. 2023How to Mask in Error Correction Code Transformer: Systematic and Double Maskingcited by 5
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  32. 2023Soft-Output Deep Neural Network-Based Decodingcited by 2

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