In multiuser MIMO systems, the required feedback rate per user increases linearly with the number of transmit antennas in order to achieve full multiplexing gain. When it comes to massive MIMO systems, the feedback overhead grows unacceptable. This motivates us to explore a novel feedback reduction scheme based on principal component analysis (PCA). The proposed PCA based feedback scheme exploits the spatial correlation characteristics of massive MIMO channel model, since transmit antennas are deployed compactly at base station (BS). In the proposed scheme, mobile station (MS) utilizes compression matrix to compress spatially correlated high-dimensional channel state information (CSI) into low-dimensional one. Then the compressed low-dimensional CSI is fed back to BS instantaneously with reduced feedback overhead and codebook search complexity. The compression matrix is attained by operating PCA on CSI which is estimated over a long-term period by MS. In order to recover high-dimensional CSI at BS, compression matrix is refreshed and fed back from MS to BS every long-term period. Numerical results and feedback overhead analysis show that the proposed PCA based feedback scheme can offer a tradeoff between system performance and feedback overhead.
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