Diffusion Denoiser Achievable Analysis for Finite Blocklength Unsourced Random Access
For the unsourced multiple access channel problem, this method provides a simple integration with existing code designs and consistent performance gains.
This work introduces a diffusion denoiser as a lightweight analysis within joint decoding for unsourced random access, achieving a strictly tighter achievable bound and at least 0.5 dB improvement in required E_b/N_0 over existing decoders.
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.