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

本文提出了基于深度学习和超分辨技术的低开销光束和功率分配方法，以应对多用户毫米波网络中用户和车辆的移动、不必要的光束重新选择和定位等难题，并通过理论和数值分析结果证明了该方法的有效性。