Diffusion-Based Noise-Adaptive Null-Space Channel Estimation for OFDM Systems

2026-07-03

AI Summary

This work addresses the challenge of achieving both high accuracy and robustness in channel estimation for OFDM systems under sparse pilot patterns, strong noise, and varying configurations. The authors formulate DMRS-aided channel estimation as a sparse linear inverse problem and propose a posterior sampling method based on diffusion models. Specifically, they employ a conditional U-Net denoiser—operating only on subsampled subcarriers—to learn the complex-valued spatial prior of the channel, and incorporate a noise-adaptive posterior correction mechanism that jointly optimizes correction coefficients and residual variance. Experimental results demonstrate that the proposed approach significantly outperforms existing estimators in terms of NMSE under 5G NR TDL and CDL channel models, while exhibiting strong robustness to variations in SNR, DMRS configurations, Doppler shifts, and distributional shifts.

Abstract

Accurate channel estimation in orthogonal frequency division multiplexing (OFDM) systems remains challenging when demodulation reference signal (DMRS) observations are sparse and noisy, and when DMRS configurations vary across deployment scenarios. This paper proposes DANCE (Diffusion-based Noise-Adaptive Null-space Channel Estimation), a diffusion-based channel estimator for OFDM systems. We formulate DMRS-aided channel estimation as a sparse linear inverse problem whose measurement operator is induced by the pilot pattern. The resulting range-null space decomposition separates the measurement-constrained range-space component from the unobserved null-space component, which is reconstructed through a learned diffusion prior. To avoid directly imposing noisy pilot samples as exact constraints, DANCE introduces a noise-adaptive posterior correction into the reverse diffusion process. The correction coefficient and the residual sampling variance are jointly calibrated according to the observation noise level, thereby reducing pilot-noise injection while retaining useful measurement information. We further design a conditional U-Net denoiser for complex-valued OFDM channel grids, where the real and imaginary components are represented as separate feature channels and downsampling is performed only along the subcarrier dimension. Simulations based on 5G NR tapped delay line (TDL) and clustered delay line (CDL) channel models show that DANCE achieves consistently lower normalized mean squared error (NMSE) than conventional estimators and diffusion-based posterior sampling methods under different signal-to-noise ratios, DMRS configurations, Doppler frequency shifts, and train-test distribution mismatches.

Problem

• channel estimation
• OFDM
• DMRS
• noise
• sparse observations

Innovation

• diffusion model
• noise-adaptive estimation
• null-space reconstruction
• OFDM channel estimation
• conditional U-Net