{"ID":2869485,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.15182","arxiv_id":"2509.15182","title":"Conditional Prior-based Non-stationary Channel Estimation Using Accelerated Diffusion Models","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.15182","url_pdf":"https://arxiv.org/pdf/2509.15182v1","authors":"[\"Muhammad Ahmed Mohsin\",\"Ahsan Bilal\",\"Muhammad Umer\",\"Asad Aali\",\"Muhammad Ali Jamshed\",\"Dean F. Hougen\",\"John M. Cioffi\"]","published":"2025-09-18T17:43:20Z","proceeding":"cs.DC","tasks":"[\"cs.DC\"]","methods":"[\"Diffusion Model\"]","has_code":false}