Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models

• URL: http://arxiv.org/abs/2506.07770v2
• Date: Wed, 23 Jul 2025 14:10:03 GMT
• Title: Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models
• Authors: Yang Wang, Yin Xu, Cixiao Zhang, Zhiyong Chen, Mingzeng Dai, Haiming Wang, Bingchao Liu, Dazhi He, Meixia Tao,
• Abstract summary: Reconfigurable intelligent surface (RIS) has been recognized as a promising technology for next-generation wireless communications.<n>The performance of RIS-assisted systems critically depends on accurate channel state information (CSI)<n>This letter proposes a novel channel estimation method for RIS-aided millimeter-wave (mmWave) systems based on diffusion models (DMs)
• Score: 32.962034180176865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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