Abstract
In this article, 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 framework.
Original language | English |
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Pages (from-to) | 15080-15085 |
Number of pages | 6 |
Journal | IEEE Transactions on Vehicular Technology |
Volume | 72 |
Issue number | 11 |
DOIs | |
Publication status | Published - 2023 Nov 1 |
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Aerospace Engineering
- Computer Networks and Communications
- Electrical and Electronic Engineering