Diffusion Denoiser Achievable Analysis for Finite Blocklength Unsourced Random Access
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 meth...
| Authors | Yuming Han, Yuxin Long |
|---|---|
| Primary category | cs.IT |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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