  This paper proposes a deep learning based power allocation (DL-PA) and hybrid
precoding technique for multiuser massive multiple-input multiple-output
(MU-mMIMO) systems. We first utilize an angular-based hybrid precoding
technique for reducing the number of RF chains and channel estimation overhead.
Then, we develop the DL-PA algorithm via a fully-connected deep neural network
(DNN). DL-PA has two phases: (i) offline supervised learning with the optimal
allocated powers obtained by particle swarm optimization based PA (PSO-PA)
algorithm, (ii) online power prediction by the trained DNN. In comparison to
the computationally expensive PSO-PA, it is shown that DL-PA greatly reduces
the runtime by 98.6%-99.9%, while closely achieving the optimal sum-rate
capacity. It makes DL-PA a promising algorithm for the real-time online
applications in MU-mMIMO systems.
