  Base station (BS) architectures for massive multi-user (MU) multiple-input
multiple-output (MIMO) wireless systems are equipped with hundreds of antennas
to serve tens of users on the same time-frequency channel. The immense number
of BS antennas incurs high system costs, power, and interconnect bandwidth. To
circumvent these obstacles, sophisticated MU precoding algorithms that enable
the use of 1-bit DACs have been proposed. Many of these precoders feature
parameters that are, traditionally, tuned manually to optimize their
performance. We propose to use deep-learning tools to automatically tune such
1-bit precoders. Specifically, we optimize the biConvex 1-bit PrecOding (C2PO)
algorithm using neural networks. Compared to the original C2PO algorithm, our
neural-network optimized (NNO-)C2PO achieves the same error-rate performance at
$\bf 2\boldsymbol\times$ lower complexity. Moreover, by training NNO-C2PO for
different channel models, we show that 1-bit precoding can be made robust to
vastly changing propagation conditions.
