Conditional Prior-Based Non-Stationary Channel Estimation Using Accelerated Diffusion Models
Year: 2026
Type: conference-paper
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-based 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. During 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 the evolution without biasing the test-time estimator. Evaluations on a 3GPP benchmark show lower NMSE across all SNR regimes than LMMSE, GMM, LSTM, and LDAMP baselines, demonstrating stable performance and strong high-SNR fidelity.
Authors: Muhammad Ahmed Mohsin, Ahsan Bilal, Muhammad Umer, Asad Aali, Muhammad Ali Jamshed, Dean F. Hougen, John M. Cioffi
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4417077983
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