  The reconfigurable intelligent surface (RIS) technology is a promising
enabler for millimeter wave (mmWave) wireless communications, as it can
potentially provide spectral efficiency comparable to the conventional massive
multiple-input multiple-output (MIMO) but with significantly lower hardware
complexity. In this paper, we focus on the estimation and projection of the
uplink RIS-aided massive MIMO channel, which can be time-varying. We propose to
let the user equipments (UE) transmit Zadoff-Chu (ZC) sequences and let the
base station (BS) conduct maximum likelihood (ML) estimation of the uplink
channel. The proposed scheme is computationally efficient: it uses ZC sequences
to decouple the estimation of the frequency and time offsets; it uses the
space-alternating generalized expectation-maximization (SAGE) method to reduce
the high-dimensional problem due to the multipaths to multiple
lower-dimensional ones per path. Owing to the estimation of the Doppler
frequency offsets, the time-varying channel state can be projected, which can
significantly lower the overhead of the pilots for channel estimation. The
numerical simulations verify the effectiveness of the proposed scheme.
