{"ID":2823234,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.00612","arxiv_id":"2601.00612","title":"WiFo-MUD: Wireless Foundation Model for Heterogeneous Multi-User Demodulator","abstract":"Multi-user signal demodulation is critical to wireless communications, directly impacting transmission reliability and efficiency. However, existing demodulators underperform in generic multi-user environments: classical demodulators struggle to balance accuracy and complexity, while deep learning-based methods lack adaptability under heterogeneous configurations. Although diffusion models have been introduced for demodulation, their flexibility remains limited for practical use. To address these issues, this work proposes WiFo-MUD, a universal diffusion-based foundation model for multi-user demodulation. The model aligns inter-user signal-to-noise ratio imbalance and performs conditional denoising via a customized backbone. Furthermore, a communication-aware consistency distillation method and a dynamic user-grouping strategy are devised to enhance inference. WiFo-MUD achieves state-of-the-art results on large-scale heterogeneous datasets, demonstrating efficient inference and strong generalization across varying system configurations.","short_abstract":"Multi-user signal demodulation is critical to wireless communications, directly impacting transmission reliability and efficiency. However, existing demodulators underperform in generic multi-user environments: classical demodulators struggle to balance accuracy and complexity, while deep learning-based methods lack ad...","url_abs":"https://arxiv.org/abs/2601.00612","url_pdf":"https://arxiv.org/pdf/2601.00612v1","authors":"[\"Zonghui Yang\",\"Shijian Gao\",\"Xuesong Cai\",\"Xiang Cheng\",\"Liuqing Yang\"]","published":"2026-01-02T08:48:46Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Diffusion Model\"]","has_code":false}