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arXiv · 2605.26580

Structured Masked Diffusion for Joint Multiuser Decoding

Taekyun Lee·Jiyoung Yun·Jeffrey G. Andrews·Hyeji Kim
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Abstract

In joint multiuser decoding, a receiver recovers a set of messages from a single noisy aggregate of many simultaneous transmissions. Classical decoders rely on rule-based mechanisms such as successive interference cancellation, joint belief propagation, or list recovery, all of which become brittle or expensive as ambiguity increases. We propose CIDER, a learned multiuser decoder with masked-diffusion refinement steps. CIDER uses demixing to prevent duplicate-row collapse and uses parity-aware propagation to provide soft guidance from the code constraints. In higher-load regimes, we further improve reliability via a lightweight quality-guided remasking step that selectively re-decodes low-confidence sequences. On commonly used error-correcting codes, CIDER matches or improves on FFT-accelerated joint belief propagation-style decoding in symbol error rate while running more than $6\times$ to over $100\times$ faster, with the speedup widening as the blocklength grows. Code is available at https://github.com/jiyunyoung/CIDER.

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Taekyun Lee, Jiyoung Yun, Jeffrey G. Andrews, Hyeji Kim. 2026-05-26. Structured Masked Diffusion for Joint Multiuser Decoding. https://arxiv.org/abs/2605.26580

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