A new sparse channel estimation for 2D MIMO-OFDM systems based on compressive sensing

We propose a new approach based on compressive sensing (CS) for channel estimation for MIMO-OFDM systems equipped with 2-Dimensional (2D) active antenna arrays. A path-based channel model which is described by delay, angle of arrival (AOA), and attenuation factor is used in this article. It is popular to assume such MIMO channels are sparse both in the delay-domain and angle-domain, and CS based method can be applied to solve the sparse channel estimation problem. The proposed sparse channel estimation algorithm is divided into three stages. We first find the positions of non-zero taps in time domain and then estimate elevation angle and azimuth angle on each tap jointly. At last, attenuation factor on each tap is obtained. Simulation results show that the proposed method achieves more effective channel estimation performance compared to least square (LS) estimation.