Channel estimation for massive MIMO with 2-D nested array deployment

The problem of channel estimation in 5G is regarded as the one of the bottleneck problems due to its complexity related with large number of antenna elements at the BS side and more narrower beams when choosing high frequency such as millimeter wave. In this paper, we study the channel estimation problem for massive MIMO with a new antenna array at the base station (BS) side. The randomly deployed single antenna user equipments (UEs) within a single cell in the cellular network comprise of a random array. Based on the geometric channel model, using multiple snapshots of beamforming and combining vectors at the BS and UEs side respectively, the problem is formulated as a sparsity-aware problem and the coordinate descent algorithm is employed to retrieve the significant channel gain. Simulation results show the effective of the algorithm under two different scenarios with high SNR and low SNR respectively and for both cases, we can find the significant paths with properly chosen penalization parameter λ.