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PCA based limited feedback scheme for massive MIMO with Kalman filter enhancing performance

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, the feedback overhead grows unacceptable. Firstly, this motivates us to explore a novel feedback reduction scheme based on principal component analysis (PCA) for massive MIMO. In the proposed scheme, mobile station (MS) utilizes compression matrix to compress spatially correlated high-dimensional channel state information (CSI) into low-dimensional representation. 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. Secondly, Kalman filter is adopted to improve the performance of the above PCA based feedback scheme, when channel estimation is non-ideal. Numerical results and theoretical analysis show that the proposed PCA based feedback scheme can offer a tradeoff between system performance and feedback overhead. Additionally, it is also verified by the simulation that Kalman filter can contribute to the system capacity enhancement in the case of non-ideal channel estimation.