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Channel-state-information (CSI) feedback methods are considered, especiallyfor massive or very large-scale multiple-input multiple-output (MIMO) systems.To extract essential information from the CSI without redundancy that arisesfrom the highly correlated antennas, a receiver transforms (sparsifies) acorrelated CSI vector to an uncorrelated sparse CSI vector by using aKarhunen-Loeve transform (KLT) matrix that consists of the eigen vectors ofcovariance matrix of CSI vector and feeds back the essential components of thesparse CSI, i.e., a principal component analysis method. A transmitter thenrecovers the original CSI through the inverse transformation of the feedbackvector. Herein, to obtain the covariance matrix at transceiver, we deriveanalytically the covariance matrix of spatially correlated Rayleigh fadingchannels based on its statistics including transmit antennas' and receiveantennas' correlation matrices, channel variance, and channel delay profile.With the knowledge of the channel statistics, the transceiver can readilyobtain the covariance matrix and KLT matrix. Compression feedback error andbit-error-rate performance of the proposed method are analyzed. Numericalresults verify that the proposed method is promising, which reducessignificantly the feedback overhead of the massive-MIMO systems with marginalperformance degradation from full-CSI feedback (e.g., feedback amount reductionby 80%, i.e., 1/5 of original CSI, with spectral efficiency reduction by only2%). Furthermore, we show numerically that, for a given limited feedbackamount, we can find the optimal number of transmit antennas to achieve thelargest spectral efficiency, which is a new design framework.

Principal Component Analysis (PCA)-based Massive-MIMO Channel Feedback