用扩散模型提升太赫兹通信中RIS的信道估计精度
Channel Estimation for RIS-Assisted mmWave Systems via Diffusion Models
- 将信道估计转化为扩散模型的逆过程，利用去噪机制恢复真实信道
- 在多种场景下性能超越现有方法，尤其在低信噪比时优势明显
- 设计轻量网络BRCNet，参数量减少显著，适合实际部署
可重构智能表面（RIS）被视为下一代无线通信的有前途技术。然而，RIS辅助系统性能高度依赖精确的信道状态信息（CSI）。为应对这一挑战，本文提出一种基于扩散模型（DMs）的新信道估计方法，适用于RIS辅助的毫米波（mmWave）系统。具体地，在扩散模型框架下，将原始信号的前向扩散过程建模为接收信号的噪声观测；随后将信道估计任务表述为反向扩散过程，并开发基于去噪扩散隐式模型（DDIMs）的采样算法以实现高效推理。此外，引入一种轻量级神经网络BRCNet，替代传统U-Net，显著降低参数量与计算复杂度。在多种场景下的大量实验表明，所提方法持续优于现有基线。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。