Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866911357220159488 |
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| author | Wang, Yang Xu, Yin Zhang, Cixiao Chen, Zhiyong Dai, Mingzeng Wang, Haiming Liu, Bingchao He, Dazhi Tao, Meixia |
| author_facet | Wang, Yang Xu, Yin Zhang, Cixiao Chen, Zhiyong Dai, Mingzeng Wang, Haiming Liu, Bingchao He, Dazhi Tao, Meixia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07770 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models Wang, Yang Xu, Yin Zhang, Cixiao Chen, Zhiyong Dai, Mingzeng Wang, Haiming Liu, Bingchao He, Dazhi Tao, Meixia Signal Processing Machine Learning 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. |
| title | Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2506.07770 |