AMP-Net Based Beamspace Channel Estimation in mmWave Massive MIMO Systems

Abstract

Due to the high channel dimensionality caused by the large number of antennas, the beamspace channel estimation is challenging in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) uplink systems. To enhance the channel estimation accuracy, a novel beamspace channel estimation method is presented by employing the benefit from the compressed sensing (CS). Specifically, the problem of beamspace channel estimation is firstly transformed into an underdetermined optimization problem based on the sparse characteristic of the beamspace channel. Then the approximate message passing (AMP) based on neural network is presented, which is called AMP-Net. When the handcrafted optimal transform is orthogonal in the AMP algorithm, a balanced convolutional neural network (CNN) is designed to select the beamspace channel estimation results under the guidance of the AMP algorithm. Finally, the iterative process is expanded into a deep network form. In the network, controllable parameters are introduced to increase the flexibility of the beamspace channel estimation process. Simulation results demonstrate that the proposed algorithm has better the estimation accuracy than conventional CS based algorithms and other network models.