Compressive sensing based channel estimation for massive MIMO systems with planar arrays

In this paper, we consider the downlink training in a massive MIMO system, where the base station (BS) is equipped with a large-scale planar array and the user equipment (UE) has a single antenna. We propose a sparsity-aware channel estimation technique to estimate the 2D angular information of the dominant channel paths, which can be subsequently used for downlink beamforming in the data transmission mode. Capitalizing on compressive sensing (CS), the idea of the proposed method is to refine the measurement matrix (beam directions) during the CSI acquisition process by exploiting the knowledge of the visibility region of a planar array. The estimation process is performed by means of a modified orthogonal matching pursuit (OMP) algorithm that takes such a refinement into account. Computer simulation results evaluate the performance of the proposed method for very short training sequences.