  This paper proposes a novel reconfigurable intelligent surface (RIS)
architecture which enables channel estimation of RIS-assisted millimeter wave
(mmWave) systems. More specifically, two channel estimation methods, namely,
direct calculation (DC) and deep learning (DL) methods, are proposed to
skillfully convert the overall channel estimation into two tasks: the channel
estimation and the angle parameter estimation of a small number of active
elements. In particular, the direct calculation method calculates the angle
parameters directly through the channel estimates of adjacent active elements
and, based on it, the DL method reduces the angle offset rate and further
improves the accuracy of angle parameter estimation. Compared with the
traditional methods, the proposed schemes reduce the complexity of the RIS
channel estimation while outperforming the beam training method in terms of
minimum square error, achievable rate, and outage probability.
