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Deep learning based predictive beamforming design
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Zhang, Juping, Zheng, Gan, Zhang, Yangyishi, Krikidis, Ioannis and Wong, Kai-Kit (2023) Deep learning based predictive beamforming design. IEEE Transactions on Vehicular Technology, 72 (6). pp. 8122-8127. doi:10.1109/tvt.2023.3238108 ISSN 0018-9545.
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WRAP-Deep-learning-based-predictive-beamforming-design-Zheng-2023.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (375Kb) | Preview |
Official URL: https://doi.org/10.1109/TVT.2023.3238108
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.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
SWORD Depositor: | Library Publications Router | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Deep learning (Machine learning), Beamforming, Neural networks (Computer science) | |||||||||||||||
Journal or Publication Title: | IEEE Transactions on Vehicular Technology | |||||||||||||||
Publisher: | IEEE | |||||||||||||||
ISSN: | 0018-9545 | |||||||||||||||
Official Date: | June 2023 | |||||||||||||||
Dates: |
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Volume: | 72 | |||||||||||||||
Number: | 6 | |||||||||||||||
Number of Pages: | 6 | |||||||||||||||
Page Range: | pp. 8122-8127 | |||||||||||||||
DOI: | 10.1109/tvt.2023.3238108 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Date of first compliant deposit: | 17 March 2023 | |||||||||||||||
Date of first compliant Open Access: | 20 March 2023 | |||||||||||||||
RIOXX Funder/Project Grant: |
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