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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.22230 |
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| _version_ | 1866909870004895744 |
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| author | Diao, Ziqi Zhou, Xingyu Liang, Le Jin, Shi |
| author_facet | Diao, Ziqi Zhou, Xingyu Liang, Le Jin, Shi |
| contents | Channel estimation for massive multiple-input multiple-output (MIMO) systems is fundamentally constrained by excessive pilot overhead and high estimation latency. To overcome these obstacles, recent studies have leveraged deep generative networks to capture the prior distribution of wireless channels. In this paper, we propose a novel estimation framework that integrates an energy-based generative diffusion model (DM) with the Metropolis-Hastings (MH) principle. By reparameterizing the diffusion process with an incorporated energy function, the framework explicitly estimates the unnormalized log-prior, while MH corrections refine the sampling trajectory, mitigate deviations, and enhance robustness, ultimately enabling accurate posterior sampling for high-fidelity channel estimation. Numerical results reveal that the proposed approach significantly improves estimation accuracy compared with conventional parameterized DMs and other baseline methods, particularly in cases with limited pilot overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22230 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust MIMO Channel Estimation Using Energy-Based Generative Diffusion Models Diao, Ziqi Zhou, Xingyu Liang, Le Jin, Shi Information Theory Signal Processing Channel estimation for massive multiple-input multiple-output (MIMO) systems is fundamentally constrained by excessive pilot overhead and high estimation latency. To overcome these obstacles, recent studies have leveraged deep generative networks to capture the prior distribution of wireless channels. In this paper, we propose a novel estimation framework that integrates an energy-based generative diffusion model (DM) with the Metropolis-Hastings (MH) principle. By reparameterizing the diffusion process with an incorporated energy function, the framework explicitly estimates the unnormalized log-prior, while MH corrections refine the sampling trajectory, mitigate deviations, and enhance robustness, ultimately enabling accurate posterior sampling for high-fidelity channel estimation. Numerical results reveal that the proposed approach significantly improves estimation accuracy compared with conventional parameterized DMs and other baseline methods, particularly in cases with limited pilot overhead. |
| title | Robust MIMO Channel Estimation Using Energy-Based Generative Diffusion Models |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2510.22230 |