Downlink channel estimation of FDD based massive MIMO using spatial partial-common sparsity modeling

Abstract

Downlink channel estimation in FDD massive MIMO systems is a challenge in 5G wireless communication systems. Using orthogonal pilots for downlink channel estimation leads to the pilot overhead problem. To cope with this problem, spatio-temporal common sparsity feature of delay domain beside the compressive sensing algorithm has used for channel estimation. In a practical affair, the spatial common sparsity of the adjacent antennas groups is not entirely separate. In this paper, we model the FDD massive MIMO downlink frequency selective channel estimation problem by a spatial partial-common sparsity, in which it is assumed that the spatial sparsity pattern of antennas in each group has a common part and an uncommon part. For the proposed model, we design a proper pilot sequence, and finally, we propose an estimation method associated with this model to solve the problem. Our proposed method has better NMSE and BER performance than reference methods in the same pilot overhead ratio, which is shown in the simulation results.