  In this paper, we develop a deep learning (DL)-guided hybrid beam and power
allocation approach for multiuser millimeter-wave (mmWave) networks, which
facilitates swift beamforming at the base station (BS). The following
persisting challenges motivated our research: (i) User and vehicular mobility,
as well as redundant beam-reselections in mmWave networks, degrade the
efficiency; (ii) Due to the large beamforming dimension at the BS, the
beamforming weights predicted by the cutting-edge DL-based methods often do not
suit the channel distributions; (iii) Co-located user devices may cause a
severe beam conflict, thus deteriorating system performance. To address the
aforementioned challenges, we exploit the synergy of supervised learning and
super-resolution technology to enable low-overhead beam- and power allocation.
In the first step, we propose a method for beam-quality prediction. It is based
on deep learning and explores the relationship between high- and low-resolution
beam images (energy). Afterward, we develop a DL-based allocation approach,
which enables high-accuracy beam and power allocation with only a portion of
the available time-sequential low-resolution images. Theoretical and numerical
results verify the effectiveness of our proposed
