  This paper proposes a model-driven deep learning (MDDL)-based channel
estimation and feedback scheme for wideband millimeter-wave (mmWave) massive
hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay
domain channels' sparsity is exploited for reducing the overhead. Firstly, we
consider the uplink channel estimation for time-division duplexing systems. 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 (BS),
we propose to jointly train the phase shift network and the channel estimator
as an auto-encoder. Particularly, 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. Moreover, we consider the downlink channel
estimation and feedback for frequency-division duplexing systems. Similarly,
the pilots at the BS and channel estimator at the users can be jointly trained
as an encoder and a decoder, respectively. Besides, to further reduce the
channel feedback overhead, only the received pilots on part of the subcarriers
are fed back to the BS, which can exploit the MMV-LAMP network to reconstruct
the spatial-frequency channel matrix. Numerical results show that the proposed
MDDL-based channel estimation and feedback scheme outperforms the
state-of-the-art approaches.
