Diffusion Denoiser Achievable Analysis for Finite Blocklength Unsourced Random Access

TL;DR

This paper introduces a diffusion denoiser-based joint decoder for finite blocklength unsourced MACs, providing tighter bounds and performance improvements over existing methods.

Contribution

It proposes a novel diffusion denoiser decoder compatible with joint decoding, offering a theoretical bound and practical performance gains.

Findings

Achieves at least 0.5 dB improvement in E_b/N_0 over existing decoders.

Derives a tighter diffusion-denoiser random-coding achievable bound.

Demonstrates consistent performance gains through simulations.

Abstract

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.