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

arXiv: https://arxiv.org/abs/2305.13929 · HTML: https://mlsanity.com/paper/2305.13929/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies](https://mlsanity.com/paper/1804.01908/) (2018)
- [Millimeter Wave Cellular Wireless Networks: Potentials and Challenges](https://mlsanity.com/paper/1401.2560/) (2014)
- [Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming](https://mlsanity.com/paper/2209.02258/) (2022)
- [Active Learning and CSI Acquisition for mmWave Initial Alignment](https://mlsanity.com/paper/1812.07722/) (2018)
- [Comparative Analysis of Initial Access Techniques in 5G mmWave Cellular Networks](https://mlsanity.com/paper/1605.00101/) (2016)
- [Multi-Gigabits Millimetre Wave Wireless Communications for 5G: From Fixed Access to Cellular Networks](https://mlsanity.com/paper/1410.4290/) (2014)

## Abstract (arXiv)

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