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arXiv · 2506.07770

Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models

Yang Wang·Yin Xu·Cixiao Zhang·Zhiyong Chen·Mingzeng Dai·Haiming Wang·Bingchao Liu·Dazhi He·Meixia Tao
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Abstract

Reconfigurable intelligent surface (RIS) has been recognized as a promising technology for next-generation wireless communications. However, the performance of RIS-assisted systems critically depends on accurate channel state information (CSI). To address this challenge, this letter proposes a novel channel estimation method for RIS-aided millimeter-wave (mmWave) systems based on diffusion models (DMs). Specifically, the forward diffusion process of the original signal is formulated to model the received signal as a noisy observation within the framework of DMs. Subsequently, the channel estimation task is formulated as the reverse diffusion process, and a sampling algorithm based on denoising diffusion implicit models (DDIMs) is developed to enable effective inference. Furthermore, a lightweight neural network, termed BRCNet, is introduced to replace the conventional U-Net, significantly reducing the number of parameters and computational complexity. Extensive experiments conducted under various scenarios demonstrate that the proposed method consistently outperforms existing baselines.

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Yang Wang, Yin Xu, Cixiao Zhang, Zhiyong Chen, Mingzeng Dai, Haiming Wang, Bingchao Liu, Dazhi He, Meixia Tao. 2025-06-09. Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models. https://doi.org/10.1109/lcomm.2025.3645078

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