Sparse Channel Estimation for OFDM Based Two-Way Relay Networks

In this paper, we present a sparse channel estimation method for orthogonal frequency division multiplexing (OFDM) based two-way relay networks (TWRN). Conventional channel estimation methods, such as least squares (LS), have been proposed to obtain channel state information (CSI) at the cost of the training resource, which reduce spectrum efficiency. However, physical measurements have verified that the wireless channels tend to exhibit sparse structures in high-dimensional spaces, e.g., delay spread, Doppler spread and space spread. With the development of compressive sensing (CS), a novel compressive channel estimation method which is called adaptive compressive matching pursuit (ACMP) algorithm is proposed by using the sparse constraint between the terminal nodes and the relay node in the TWRN. Simulation results confirm that ACMP channel estimation method provides significant improvement in mean square error (MSE) performance compared to the conventional channel estimation methods.