Model-Driven Deep Learning Based Channel Estimation for Millimeter-Wave Massive Hybrid MIMO Systems

In this paper, we propose a model-driven deep learning (MDDL)-based channel estimation solution for wideband millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, where we consider the channels' sparsity in angle domain. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (RS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Specifically, by exploiting the channels' structured sparsity from an a priori model and learning the integrated trainable parameters from the data samples, the proposed multiple-measurement-vectors learned approximate message passing (MMV-LAMP) network with the devised redundant dictionary can jointly recover multiple subcarriers' channels with significantly enhanced performance. Numerical results show that the proposed MDDL-based channel estimation scheme outperforms the state-of-the-art approaches.