用一致性模型实现一步纠错，速度提升30倍以上
Syndrome-Flow Consistency Model Achieves One-step Denoising Error Correction Codes
- 通过软校验条件重构反向概率流，实现平滑去噪轨迹
- 在多个基准上比特错误率低于变压器解码器，帧错误率更低
- 适合低延迟通信场景，推理速度比扩散模型快30至100倍
纠错码（ECC）是可靠数字通信的基础，但设计准确且计算高效的神经解码器仍具挑战。近期的去噪扩散解码器虽达最先进性能，但其迭代采样限制了低延迟场景下的实用性。一致性模型（CMs）为高保真一步解码提供了可能，但将其应用于ECC面临重大挑战：纠错的离散性导致解码轨迹极不连续，难以适配简单的连续时间步参数化。为此，我们通过软校验条件重新参数化反向概率流常微分方程（PF-ODE），构建出信号失真的平滑轨迹。基于此，提出误差校正校验流一致性模型（ECCFM），一种专为ECC任务设计的模型无关框架，确保模型能从任意噪声信号直接一步学习到原始码字。在多个基准测试中，ECCFM达到比基于变压器的解码器更低的比特错误率（BER）和帧错误率（FER），同时推理速度比迭代去噪扩散解码器快30至100倍。
原文摘要 · Abstract (English)
Error Correction Codes (ECC) are fundamental to reliable digital communication, yet designing neural decoders that are both accurate and computationally efficient remains challenging. Recent denoising diffusion decoders achieve state-of-the-art performance, but their iterative sampling limits practicality in low-latency settings. To bridge this gap, consistency models (CMs) offer a potential path to high-fidelity one-step decoding. However, applying CMs to ECC presents a significant challenge: the discrete nature of error correction means the decoding trajectory is highly non-smooth, making it incompatible with a simple continuous timestep parameterization. To address this, we re-parameterize the reverse Probability Flow Ordinary Differential Equation (PF-ODE) by soft-syndrome condition, providing a smooth trajectory of signal corruption. Building on this, we propose the Error Correction Syndrome-Flow Consistency Model (ECCFM), a model-agnostic framework designed specifically for ECC task, ensuring the model learns a smooth trajectory from any noisy signal directly to the original codeword in a single step. Across multiple benchmarks, ECCFM attains lower bit-error-rate (BER) and frame-error-rate (FER) than transformer-based decoders, while delivering inference speeds 30x to 100x faster than iterative denoising diffusion decoders.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。