WiFo-MUD: Wireless Foundation Model for Heterogeneous Multi-User Demodulator
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915704420171776 |
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| author | Yang, Zonghui Gao, Shijian Cai, Xuesong Cheng, Xiang Yang, Liuqing |
| author_facet | Yang, Zonghui Gao, Shijian Cai, Xuesong Cheng, Xiang Yang, Liuqing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00612 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | WiFo-MUD: Wireless Foundation Model for Heterogeneous Multi-User Demodulator Yang, Zonghui Gao, Shijian Cai, Xuesong Cheng, Xiang Yang, Liuqing Signal Processing 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. |
| title | WiFo-MUD: Wireless Foundation Model for Heterogeneous Multi-User Demodulator |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.00612 |