Diffusion Denoiser Achievable Analysis for Finite Blocklength Unsourced Random Access

Yuming Han; Yuxin Long

cs.ITApr 10, 2026

Diffusion Denoiser Achievable Analysis for Finite Blocklength Unsourced Random Access

Authors: Yuming Han, Yuxin Long

Organizations: Texas A&M University, Department of ECE, College Station, TX 77843, USA · University of Leeds, Institute for Transport Studies, Leeds, LS2 9JT, UK

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.

Figures & tables

Figure 1: The required energy per bit to achieve $\epsilon=0.001$ as a function of the number of active users for Theorem 1 and different achievable schemes.

Figure 2: The bounds on the probability of error $\epsilon$ as a function of $E_{b}/N_{0}$ for Theorem 1 and different achievable schemes.

Diffusion-Based Denoising

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Neural channel decoder, as a data-driven channel decoding strategy, has shown very promising improvement on error-correcting capability over the classical methods. However, the success of those deep learning-based decoder comes at the cost of drastically increased model storage and computational complexity, hindering their practical adoptions in real-world time-sensitive resource-sensitive communication and storage systems. To address this challenge, we propose an efficient variational diffusion model-based channel decoder, which effectively integrates the domain-specific belief propagation process to the modern diffusion model. By reaping the low-cost benefits of belief propagation and strong learning capability of diffusion model, our proposed neural decoder simultaneously achieves very low cost and high error-correcting performance. Experimental results show that, compared with the state-of-the-art neural channel decoders, our model provides a feasible solution for practical deployment via achieving the best decoding performance with significantly reduced computational cost and model size.

Abstract

Diffusion-Based Denoising

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