  This paper studies the estimation of cascaded channels in passive intelligent
reflective surface (IRS)- aided multiple-input multiple-output (MIMO) systems
employing hybrid precoders and combiners. We propose a low-complexity solution
that estimates the channel parameters progressively. The angles of departure
(AoDs) and angles of arrival (AoAs) at the transmitter and receiver,
respectively, are first estimated using inductive matrix completion (IMC)
followed by root-MUSIC based super-resolution spectrum estimation.
Forward-backward spatial smoothing (FBSS) is applied to address the coherence
issue. Using the estimated AoAs and AoDs, the training precoders and combiners
are then optimized and the angle differences between the AoAs and AoDs at the
IRS are estimated using the least squares (LS) method followed by FBSS and the
root-MUSIC algorithm. Finally, the composite path gains of the cascaded channel
are estimated using on-grid sparse recovery with a small-size dictionary. The
simulation results suggest that the proposed estimator can achieve improved
channel parameter estimation performance with lower complexity as compared to
several recently reported alternatives, thanks to the exploitation of the
knowledge of the array responses and low-rankness of the channel using
low-complexity algorithms at all the stages.
