  An reconfigurable intelligent surface (RIS) can be used to establish
line-of-sight (LoS) communication when the direct path is compromised, which is
a common occurrence in a millimeter wave (mmWave) network. In this paper, we
focus on the uplink channel estimation of a such network. We formulate this as
a sparse signal recovery problem, by discretizing the angle of arrivals (AoAs)
at the base station (BS). On-grid and off-grid AoAs are considered separately.
In the on-grid case, we propose an algorithm to estimate the direct and RIS
channels. Neural networks trained based on supervised learning is used to
estimate the residual angles in the off-grid case, and the AoAs in both cases.
Numerical results show the performance gains of the proposed algorithms in both
cases.
