Integrating a trained diffusion denoiser into joint decoding yields a strictly tighter random-coding achievable bound for finite blocklength unsourced multiple access, with simulations showing at least 0.5 dB Eb/N0 improvement.
Diffusion Denoiser Achievable Analysis for Finite Blocklength Unsourced Random Access
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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.
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2026 1verdicts
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Diffusion Denoiser Achievable Analysis for Finite Blocklength Unsourced Random Access
Integrating a trained diffusion denoiser into joint decoding yields a strictly tighter random-coding achievable bound for finite blocklength unsourced multiple access, with simulations showing at least 0.5 dB Eb/N0 improvement.