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This paper studies the estimation of cascaded channels in passive intelligentreflective surface (IRS)- aided multiple-input multiple-output (MIMO) systemsemploying hybrid precoders and combiners. We propose a low-complexity solutionthat 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 coherenceissue. Using the estimated AoAs and AoDs, the training precoders and combinersare then optimized and the angle differences between the AoAs and AoDs at theIRS are estimated using the least squares (LS) method followed by FBSS and theroot-MUSIC algorithm. Finally, the composite path gains of the cascaded channelare estimated using on-grid sparse recovery with a small-size dictionary. Thesimulation results suggest that the proposed estimator can achieve improvedchannel parameter estimation performance with lower complexity as compared toseveral recently reported alternatives, thanks to the exploitation of theknowledge of the array responses and low-rankness of the channel usinglow-complexity algorithms at all the stages.

Inductive Matrix Completion and Root-MUSIC-Based Channel Estimation for Intelligent Reflecting Surface (IRS)-Aided Hybrid MIMO Systems