This article 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.

翻译：本文旨在在估计可重新配置的智能表面(RIS)转发的无线信道时减少巨大的实验性间接费用。由于在解决非线性绘图问题时有令人信服的深层次学习的把握,拟议办法只激活一部分RIS元素,并利用相应的级联频道估计来预测另一部分。通过合成深层神经网络(DNN)、直接信道和活跃的级联频道,首先按顺序估算,然后对不活跃的RIS元素进行频道预测。为这一合成DNN开发了三阶段培训战略。根据模拟结果,拟议的深层次学习方法有效地减少了试点间接费用,保证了可靠的估算准确性。