Spatial channel covariance information can replace full knowledge of theentire channel matrix for designing analog precoders in hybridmultiple-input-multiple-output (MIMO) architecture. Spatial channel covarianceestimation, however, is challenging for the hybrid MIMO architecture becausethe estimator operating at baseband can only obtain a lower dimensionalpre-combined signal through fewer radio frequency (RF) chains than antennas. Inthis paper, we propose two approaches for covariance estimation based oncompressive sensing techniques. One is to apply a time-varying sensing matrix,and the other is to exploit the prior knowledge that the covariance matrix isHermitian. We present the rationale of the two ideas and validate thesuperiority of the proposed methods by theoretical analysis and numericalsimulations. We conclude the paper by extending the proposed algorithms fromnarrowband massive MIMO systems with a single receive antenna to widebandsystems with multiple receive antennas.
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