  Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) has
been regarded to be an emerging solution for the next generation of
communications, in which hybrid analog and digital precoding is an important
method for reducing the hardware complexity and energy consumption associated
with mixed signal components. However, the fundamental limitations of the
existing hybrid precoding schemes is that they have high computational
complexity and fail to fully exploit the spatial information. To overcome these
limitations, this paper proposes, a deep-learning-enabled mmWave massive MIMO
framework for effective hybrid precoding, in which each selection of the
precoders for obtaining the optimized decoder is regarded as a mapping relation
in the deep neural network (DNN). Specifically, the hybrid precoder is selected
through training based on the DNN for optimizing precoding process of the
mmWave massive MIMO. Additionally, we present extensive simulation results to
validate the excellent performance of the proposed scheme. The results exhibit
that the DNN-based approach is capable ofminimizing the bit error ratio (BER)
and enhancing spectrum efficiency of the mmWave massive MIMO, which achieves
better performance in hybrid precoding compared with conventional schemes while
substantially reducing the required computational complexity.
