Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks

• URL: http://arxiv.org/abs/2305.13929v1
• Date: Mon, 8 May 2023 05:40:54 GMT
• Title: Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks
• Authors: Yuwen Cao, Tomoaki Ohtsuki, Setareh Maghsudi, and Tony Q. S. Quek
• Abstract summary: We develop a deep learning (DL)-guided hybrid beam and power allocation approach for millimeter-wave (mmWave) networks. We exploit the synergy of supervised learning and super-resolution technology to enable low-overhead beam- and power allocation.
• Score: 80.37827344656048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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