This letter aims to reduce huge pilot overhead when estimating the reconfigurable intelligent surface (RIS) relayed wireless channel. Motivated by the compelling grasp of deep learning in tackling nonlinear mapping problems, the proposed approach only activates a part of RIS elements and utilizes the corresponding cascaded channel estimate to predict another part. Through a synthetic deep neural network (DNN), the direct channel and active cascaded channel are first estimated sequentially, followed by the channel prediction for the inactive RIS elements. A three-stage training strategy is developed for this synthetic DNN. From simulation results, the proposed deep learning based approach is effective in reducing the pilot overhead and guaranteeing the reliable estimation accuracy.

中文翻译：

---

可重构智能表面辅助 MIMO 系统的深度多级 CSI 采集

这封信旨在在估计可重构智能表面 (RIS) 中继无线信道时减少巨大的导频开销。受深度学习在处理非线性映射问题方面令人信服的掌握的推动，所提出的方法仅激活一部分 RIS 元素，并利用相应的级联通道估计来预测另一部分。通过合成深度神经网络 (DNN)，首先依次估计直接通道和活动级联通道，然后对非活动 RIS 元素进行通道预测。为这个合成 DNN 开发了一个三阶段训练策略。从仿真结果来看，所提出的基于深度学习的方法有效地减少了导频开销并保证了可靠的估计精度。