Compressive sensing-based sparse channel estimation method for MIMO-OFDM systems

Channel equalization and coherent detection require accurate channel state information (CSI) at the receiver for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The conventional linear recovery methods, such as least squares (LS) and minimum mean square error (MMSE), are widely adapted in channel estimation under the assumption of rich multipath. However, numerous physical measurements have verified that the practical multipath channels tend to exhibit sparse structures. In this paper, exploiting the channel sparsity, we propose a compressive sensing-based CoSaMP recovery algorithm for MIMO-OFDM sparse channel estimation. Simulations show that the compressive sensing estimation method can obtain the accurate CSI with fewer pilots than conventional linear estimation for MIMO-OFDM systems at the cost of less computational complexity. The proposed method can greatly improve the spectrum efficiency for MIMO-OFDM systems.