Beamspace Channel Estimation in mmWave Systems Via Cosparse Image Reconstruction Technique

This paper considers the beamspace channel estimation problem in three-dimensional (3D) lens antenna array under a millimeter-wave communication system. We analyze the focusing capability of the 3D lens antenna array and the sparsity of the beamspace channel response matrix. Considering the analysis, we observe that the channel matrix can be treated as a two-dimensional (2D) natural image; that is, the channel is sparse and the changes between most of adjacent elements are subtle. Thus, for the channel estimation, we incorporate an image reconstruction technique called sparse noninformative parameter estimator-based cosparse analysis approximate message passing for imaging (SCAMPI) algorithm. The SCAMPI algorithm is faster and more accurate than earlier algorithms such as orthogonal matching pursuit and support detection algorithms. To further improve the SCAMPI algorithm, we model the channel distribution as a generic Gaussian mixture (GM) probability and embed the expectation-maximization learning algorithm into the SCAMPI algorithm to learn the parameters in the GM probability. We show that the GM probability outperforms the common uniform distribution used in image reconstruction. We also introduce a phase-shifter-reduced selection network structure to decrease the power consumption of the system and prove that the SCAMPI algorithm is robust even if the number of phase shifters is reduced by 10%.