LAUSR

This paper proposes affine-precoded superimposed pilot (SIP) design, followed by channel state information (CSI) estimation techniques for millimeter wave (mmWave) MIMO-OFDM systems. Toward this end, a multiple measurement vector (MMV) sparse Bayesian learning (SBL)-based SIP (M-SIP) technique is initially derived to exploit the simultaneous-sparsity of the beamspace channel vector across the subcarriers, thereby leading to improved performance in comparison to the existing single measurement vector (SMV)-based SBL and other techniques. This is subsequently extended to the data-aided J-SIP technique that performs joint channel estimation and data detection, and is shown to ultimately yield a better mean squared error (MSE) of the channel estimate and bit error-rate (BER). The SIP-based simultaneous-sparse channel estimation framework is extended to time-selective wideband, i.e. doubly-selective mmWave MIMO channels, via the MMV sparse Kalman filtering-based SIP (MK-SIP) technique for tracking the CSI, and subsequently also to the joint Kalman filtering-based SIP (JK-SIP) for data-aided CSI acquisition. Bayesian Cramér-Rao bounds are determined both for quasistatic and doubly-selective mmWave MIMO-OFDM channel estimation. Simulation results, which also incorporate practical channel realizations, are provided to demonstrate performance improvement considering various metrics, such as MSE and BER.