用扩散去噪器提升有限块长无源随机接入的解码性能
Diffusion Denoiser Achievable Analysis for Finite Blocklength Unsourced Random Access
- 在联合解码中引入轻量级扩散去噪器，直接处理信道输出噪声
- 理论推导出更紧致的可达性界，仿真显示至少提升0.5 dB性能
- 可无缝集成现有码设计，适合通信系统优化研究者
Polyanskiy提出了有限块长下无源多址信道（MAC）问题的框架，用户使用共享码本。然而，现有方法在联合解码前处理信道噪声。本文提出一种与扩散去噪器兼容的解码器，作为联合解码中的轻量分析模块。评分网络在从信道输出分布中采样的样本上训练，便于与现有码设计集成。理论分析中，我们推导出一个严格更紧的扩散去噪器随机编码可达界。在现有解码器（包括FASURA、MSUG-MRA和基于导频的方法）上的仿真表明，在固定误码率目标下，性能均有提升，所需E_b/N_0至少降低0.5 dB。
原文摘要 · Abstract (English)
Polyanskiy proposed a framework for the unsourced multiple access channel (MAC) problem where users employ a common codebook in the finite blocklength regime. However, existing approaches handle channel noise before the joint decoder. In this work, we introduce a decoder compatible diffusion denoiser as a lightweight analysis within joint decoding. The score network is trained on samples drawn from the channel output distribution, making the method easy to integrate with existing code designs. In our theoretical analysis, we derive a diffusion-denoiser random-coding achievable bound that is strictly tighter. Simulations on existing decoders, including FASURA, MSUG-MRA and pilot-based method, show consistent performance gains with at least a $0.5$ $\mathrm{dB}$ improvement in required $\mathrm{E_b/N_0}$ at a fixed error target.
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