  Massive multiuser multiple-input multiple-output (MU-MIMO) has been the
mainstream technology in fifth-generation wireless systems. To reduce high
hardware costs and power consumption in massive MU-MIMO, low-resolution
digital-to-analog converters (DAC) for each antenna and radio frequency (RF)
chain in downlink transmission is used, which brings challenges for precoding
design. To circumvent these obstacles, we develop a model-driven deep learning
(DL) network for massive MU-MIMO with finite-alphabet precoding in this
article. The architecture of the network is specially designed by unfolding an
iterative algorithm. Compared with the traditional state-of-the-art techniques,
the proposed DL-based precoder shows significant advantages in performance,
complexity, and robustness to channel estimation error under Rayleigh fading
channel.
