Compressive sensing-based channel estimation for massive multiuser MIMO systems

We propose a new approach based on compressive sensing (CS) for the channel matrix estimation problem for “massive” (or large-scale) multiuser (MU) multiple-input multiple-output (MIMO) systems. The system model includes a base station (BS) equipped with a very large number of antennas communicating simultaneously with a large number of autonomous single-antenna user terminals (UTs), over a realistic physical channel with finite scattering model. Based on the idea that the degree of freedom of the channel matrix is smaller than its large number of free parameters, a low-rank matrix approximation based on CS is proposed and solved via a quadratic semidefine programming (SDP). Our analysis and experimental results suggest that the proposed method outperforms the existing ones in terms of estimation error performance or training transmit power, without requiring any knowledge about the statistical distribution or physical parameters of the propagation channel.