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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

TL;DR

This paper tackles the challenge of acquiring accurate channel state information (CSI) in RIS-assisted mmWave systems by introducing a diffusion-model–based channel estimation framework. It formulates the estimation as a reverse diffusion process and employs a step-matching DDIM sampling strategy to adapt to varying SNR conditions, paired with a lightweight BRCNet backbone to reduce complexity. The method demonstrates superior performance over traditional and prior diffusion-based baselines, achieving near-U-Net accuracy with a fraction of parameters. The approach offers a practical, scalable solution for RIS CSI in dynamic wireless environments with potential for real-world deployment.

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.

Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models

TL;DR

This paper tackles the challenge of acquiring accurate channel state information (CSI) in RIS-assisted mmWave systems by introducing a diffusion-model–based channel estimation framework. It formulates the estimation as a reverse diffusion process and employs a step-matching DDIM sampling strategy to adapt to varying SNR conditions, paired with a lightweight BRCNet backbone to reduce complexity. The method demonstrates superior performance over traditional and prior diffusion-based baselines, achieving near-U-Net accuracy with a fraction of parameters. The approach offers a practical, scalable solution for RIS CSI in dynamic wireless environments with potential for real-world deployment.

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.

Paper Structure

This paper contains 12 sections, 15 equations, 3 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Signal Model
  5. Proposed Method
  6. Training Algorithm of the Proposed Method
  7. Sampling Algorithm of the Proposed Method
  8. Network Architecture Design
  9. Numerical Results
  10. Experiment Setup
  11. Results and Analysis
  12. Conclusion

Figures (3)

  • Figure 1: RIS-assisted mmWave communication systems.
  • Figure 2: The architecture of the proposed network.
  • Figure 3: NMSE performance versus SNR under different scenarios:(a) UMi, $f_c=28$GHz. (b) UMi, $f_c=73$GHz. (c) InH, $f_c=28$GHz. (d) InH, $f_c=73$GHz.