Sparse Bayesian Learning-based Channel Estimation for IRS-aided Millimeter Wave Massive MIMO Systems

Intelligent reflecting surface (IRS)-aided millimeter wave (mmWave) systems are potential contenders for large scale deployment in 5G and beyond communication. Owing to the passive nature of IRS systems, the acquisition of accurate channel state information in bandwidth constrained scenarios becomes a highly challenging task. This work leverages the inherent sparsity associated with cascaded channels of IRS mmWave systems to develop novel sparse Bayesian learning (SBL)-based channel estimation algorithms. The underlying row-wise temporal correlation of the effective angular domain sparse channel matrix is first theoretically demonstrated followed by the development of the temporal SBL channel estimation approach. Further, utilizing the scaling property of cascaded channels for multiple users, low complex variants of the proposed SBL solutions are developed. Simulation results demonstrate significant improvement of the proposed SBL schemes over existing techniques.