  Inspired by the remarkable learning and prediction performance of deep neural
networks (DNNs), we apply one special type of DNN framework, known as
model-driven deep unfolding neural network, to reconfigurable intelligent
surface (RIS)-aided millimeter wave (mmWave) single-input multiple-output
(SIMO) systems. We focus on uplink cascaded channel estimation, where known and
fixed base station combining and RIS phase control matrices are considered for
collecting observations. To boost the estimation performance and reduce the
training overhead, the inherent channel sparsity of mmWave channels is
leveraged in the deep unfolding method. It is verified that the proposed deep
unfolding network architecture can outperform the least squares (LS) method
with a relatively smaller training overhead and online computational
complexity.
