A Novel Sparse Channel Estimation Method for Multipath MIMO-OFDM Systems

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is the promising technology for next generation communication systems due to high throughput. Due to the coherent receiving and demodulation at the receiver, accurate channel state information (CSI) is indispensable. Conventional rich assumption-based channel estimators have been proposed at the cost of enough training resource which leads to extra spectrum waste. However, physical measurements have verified that the wireless channels tend to exhibit sparse structure in high-dimensional space, e.g., delay spread, Doppler spread and space spread. Some sparse channel estimation methods for the MIMO-OFDM have been proposed. These estimation methods utilize either greedy algorithm or convex optimization. In this paper, we propose a novel sparse channel estimation method using sparse cognitive matching pursuit (SCMP) algorithm. Compared to other compressive algorithms in the state of art, the major innovation of the SCMP sparse channel estimation method (SCMP-SCE) is the ability of obtaining the accurate CSI without prior information of sparsity. Simulation results confirm that the proposed method has better estimation performance and lower estimation complexity.