Plug-and-Play Consistency Models for MIMO Channel Estimation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908993786478592 |
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| author | Li, Jinlong Yang, Peng Xiong, Zehui Cao, Xianbin |
| author_facet | Li, Jinlong Yang, Peng Xiong, Zehui Cao, Xianbin |
| contents | Consistency models (CMs) learn a consistent mapping from multiple noise levels to the data endpoint and can therefore perform generative inference in one or a few steps. This property makes them attractive as learned priors for low-latency inverse problems. Multiple-input multiple-output (MIMO) channel estimation under limited pilot overhead can be formulated as a high-dimensional linear inverse problem with an explicit measurement matrix, where data consistency alone is often insufficient for stable angular-domain channel recovery. This paper applies the plug-and-play consistency model (PnP-CM) framework to pilot-aided MIMO channel estimation. The PnP-CM inference procedure enforces the pilot observation model in the data-consistency update and invokes a pretrained CM denoiser in the prior update, thereby recovering the angular-domain channel vector within a small number of outer iterations. Preliminary experiments validate the feasibility of using CMs as low-latency channel-estimation priors and show that adaptive parameter scheduling and cross-scenario robustness remain important directions for further improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23595 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Plug-and-Play Consistency Models for MIMO Channel Estimation Li, Jinlong Yang, Peng Xiong, Zehui Cao, Xianbin Signal Processing Consistency models (CMs) learn a consistent mapping from multiple noise levels to the data endpoint and can therefore perform generative inference in one or a few steps. This property makes them attractive as learned priors for low-latency inverse problems. Multiple-input multiple-output (MIMO) channel estimation under limited pilot overhead can be formulated as a high-dimensional linear inverse problem with an explicit measurement matrix, where data consistency alone is often insufficient for stable angular-domain channel recovery. This paper applies the plug-and-play consistency model (PnP-CM) framework to pilot-aided MIMO channel estimation. The PnP-CM inference procedure enforces the pilot observation model in the data-consistency update and invokes a pretrained CM denoiser in the prior update, thereby recovering the angular-domain channel vector within a small number of outer iterations. Preliminary experiments validate the feasibility of using CMs as low-latency channel-estimation priors and show that adaptive parameter scheduling and cross-scenario robustness remain important directions for further improvement. |
| title | Plug-and-Play Consistency Models for MIMO Channel Estimation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.23595 |