Conditional Prior-based Non-stationary Channel Estimation Using Accelerated Diffusion Models
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916956560424960 |
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| author | Mohsin, Muhammad Ahmed Bilal, Ahsan Umer, Muhammad Aali, Asad Jamshed, Muhammad Ali Hougen, Dean F. Cioffi, John M. |
| author_facet | Mohsin, Muhammad Ahmed Bilal, Ahsan Umer, Muhammad Aali, Asad Jamshed, Muhammad Ali Hougen, Dean F. Cioffi, John M. |
| contents | Wireless channels in motion-rich urban microcell (UMi) settings are non-stationary; mobility and scatterer dynamics shift the distribution over time, degrading classical and deep estimators. This work proposes conditional prior diffusion for channel estimation, which learns a history-conditioned score to denoise noisy channel snapshots. A temporal encoder with cross-time attention compresses a short observation window into a context vector, which captures the channel's instantaneous coherence and steers the denoiser via feature-wise modulation. In inference, an SNR-matched initialization selects the diffusion step whose marginal aligns with the measured input SNR, and the process follows a shortened, geometrically spaced schedule, preserving the signal-to-noise trajectory with far fewer iterations. Temporal self-conditioning with the previous channel estimate and a training-only smoothness penalty further stabilizes evolution without biasing the test-time estimator. Evaluations on a 3GPP benchmark show lower NMSE across all SNRs than LMMSE, GMM, LSTM, and LDAMP baselines, demonstrating stable performance and strong high SNR fidelity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15182 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Conditional Prior-based Non-stationary Channel Estimation Using Accelerated Diffusion Models Mohsin, Muhammad Ahmed Bilal, Ahsan Umer, Muhammad Aali, Asad Jamshed, Muhammad Ali Hougen, Dean F. Cioffi, John M. Distributed, Parallel, and Cluster Computing Wireless channels in motion-rich urban microcell (UMi) settings are non-stationary; mobility and scatterer dynamics shift the distribution over time, degrading classical and deep estimators. This work proposes conditional prior diffusion for channel estimation, which learns a history-conditioned score to denoise noisy channel snapshots. A temporal encoder with cross-time attention compresses a short observation window into a context vector, which captures the channel's instantaneous coherence and steers the denoiser via feature-wise modulation. In inference, an SNR-matched initialization selects the diffusion step whose marginal aligns with the measured input SNR, and the process follows a shortened, geometrically spaced schedule, preserving the signal-to-noise trajectory with far fewer iterations. Temporal self-conditioning with the previous channel estimate and a training-only smoothness penalty further stabilizes evolution without biasing the test-time estimator. Evaluations on a 3GPP benchmark show lower NMSE across all SNRs than LMMSE, GMM, LSTM, and LDAMP baselines, demonstrating stable performance and strong high SNR fidelity. |
| title | Conditional Prior-based Non-stationary Channel Estimation Using Accelerated Diffusion Models |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.15182 |