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Deep Learning Based Predictive Beamforming Design

Zhang, J; Zheng, G; Zhang, Y; Krikidis, I; Wong, KK; (2023) Deep Learning Based Predictive Beamforming Design. IEEE Transactions on Vehicular Technology pp. 1-6. 10.1109/TVT.2023.3238108. (In press). Green open access

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Abstract

This paper investigates deep learning techniques to predict transmit beamforming based on only historical channel data without current channel information in the multiuser multiple-input-single-output downlink. This will significantly reduce the channel estimation overhead and improve the spectrum efficiency especially in high-mobility vehicular communications. Specifically, we propose a joint learning framework that incorporates channel prediction and power optimization, and produces prediction for transmit beamforming directly. In addition, we propose to use the attention mechanism in the Long Short-Term Memory Recurrent Neural Networks to improve the accuracy of channel prediction. Simulation results using both a simple autoregressive process model and the more realistic 3GPP spatial channel model verify that our proposed predictive beamforming scheme can significantly improve the effective spectrum efficiency compared to traditional channel estimation and the method that separately predicts channel and then optimizes beamforming.

Type: Article
Title: Deep Learning Based Predictive Beamforming Design
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TVT.2023.3238108
Publisher version: https://doi.org/10.1109/TVT.2023.3238108
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: channel prediction, beamforming, deep learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10164834
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