  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.
