Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks
This work addresses performance degradation in mmWave networks for mobile users and devices, but it is incremental as it builds on existing DL-based methods with super-resolution integration.
The paper tackles the problem of inefficient beam and power allocation in multiuser millimeter-wave networks due to mobility, high-dimensional beamforming, and beam conflicts, by developing a deep learning and super-resolution-guided approach that achieves high-accuracy allocation with reduced overhead, as verified by theoretical and numerical results.
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