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In this paper, channel estimation for Millimeter Wave (mmWave) Multiple Input Multiple Output (MIMO) communication system is presented. In the frequency domain, the Sparse Bayesian Learning-based Kalman Filter (SBL-KF) scheme is developed to estimate the sparse channel vector of hybrid mmWave MIMO architecture. The mmWave MIMO hybrid architecture provides reduced power loss and achievable data rates at mmWave frequencies. Frequency-selective channels are sparse at mmWave frequencies. The recovery of the sparse channel is estimated using different channel estimation methods. The hybrid precoding technique achieves both multiplexing gain and array gain in mmWave channels. Simulation results of the proposed SBL-KF scheme show low estimation error over the existing Orthogonal Matching Pursuit, Simultaneous Orthogonal Matching Pursuit, Simultaneous Weighted Orthogonal Matching Pursuit algorithm and SBL scheme. The complexity of the online estimation SBL-KF algorithm is low and it is ideally suited for implementation in an efficient mmWave MIMO system.

Sparse Bayesian Learning Kalman Filter-based Channel Estimation for Hybrid Millimeter Wave MIMO Systems: A Frequency Domain Approach