Diffusion Model-Based Channel Estimation for RIS-Aided Communication Systems

In this letter, we investigate the channel estimation problem in the reconfigurable intelligent surface (RIS)-aided wireless communication system. To recover channels accurately, we propose a novel diffusion model-based channel estimation method for combating the noise at the receiver effectively. Specifically, the channel recovery is accomplished via a continuous prior sampling process, where the prior information is derived from a U-Net that undergoes likelihood-based training. Additionally, in order to reduce the adverse effect of the phase noise at the RIS, we incorporate the gradient descent value of RIS phase into the sampling process. Simulation results demonstrate that the proposed method surpasses baselines in estimation accuracy, achieving a superior performance of more than 3.2 dB. Furthermore, the proposed method exhibits remarkable robustness, working effectively under different noise levels.