  Acquiring accurate channel state information (CSI) at an access point (AP) is
challenging for wideband millimeter wave (mmWave) ultra-massive multiple-input
and multiple-output (UMMIMO) systems, due to the high-dimensional channel
matrices, hybrid near- and far- field channel feature, beam squint effects, and
imperfect hardware constraints, such as low-resolution analog-to-digital
converters, and in-phase and quadrature imbalance. To overcome these
challenges, this paper proposes an efficient downlink channel estimation (CE)
and CSI feedback approach based on knowledge and data dual-driven deep learning
(DL) networks. Specifically, we first propose a data-driven residual neural
network de-quantizer (ResNet-DQ) to pre-process the received pilot signals at
user equipment (UEs), where the noise and distortion brought by imperfect
hardware can be mitigated. A knowledge-driven generalized multiple measurement
vector learned approximate message passing (GMMV-LAMP) network is then
developed to jointly estimate the channels by exploiting the approximately same
physical angle shared by different subcarriers. In particular, two wideband
redundant dictionaries (WRDs) are proposed such that the measurement matrices
of the GMMV-LAMP network can accommodate the far-field and near-field beam
squint effect, respectively. Finally, we propose an encoder at the UEs and a
decoder at the AP by a data-driven CSI residual network (CSI-ResNet) to
compress the CSI matrix into a low-dimensional quantized bit vector for
feedback, thereby reducing the feedback overhead substantially. Simulation
results show that the proposed knowledge and data dual-driven approach
outperforms conventional downlink CE and CSI feedback methods, especially in
the case of low signal-to-noise ratios.
