Low Complexity Compressed Sensing Based Channel Estimation in 3D MIMO Systems

By exploiting the spatial correlation in spatial domain, a three-dimensional (3D) pilot aided channel estimation (PACE) has been proposed to improve the mean-square error (MSE) performance in 3D multiple- input multiple-output (MIMO) systems. However, with the development of 3D MIMO technique, there are increasing number antenna ports in a limited space. The pilot overhead in 3D PACE method which increase linearly with the antenna number becomes unacceptable. Since compressed sensing (CS) technique ignoring the theoretic upper limit in the pilot spacing derived by the sampling theorem has been successfully applied to pilot aided 2D sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems, we introduce the CS technique to 3D pilot aided channel estimation to reduce pilot overhead in 3D MIMO systems. Moreover, a random search method based non-uniform pilot allocation algorithm with low computational complexity is proposed to further improve the CS performance. Simulation results demonstrate that compared to the traditional evenly pilot for 3D PACE, our proposed non-uniform pilot for CS-based channel estimation its average gain can improves about 3.58dB with the same pilot overhead. The result also shows that by employing the CS-based channel estimation, pilot overhead can be sharply reduced without estimation accuracy loss.