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Fast Meta Learning for Adaptive Beamforming

Zhang, J; Yuan, Y; Zheng, G; Krikidis, I; Wong, KK; (2021) Fast Meta Learning for Adaptive Beamforming. In: ICC 2021 - IEEE International Conference on Communications. IEEE: Montreal, QC, Canada. Green open access

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Abstract

This paper studies the deep learning based adaptive downlink beamforming solution for the signal-to-interference-plus-noise ratio balancing problem. Adaptive beamforming is an important approach to enhance the performance in dynamic wireless environments in which testing channels have different distributions from training channels. We propose an adaptive method to achieve fast adaptation of beamforming based on the principle of meta learning. Specifically, our method first learns an embedding model by training a deep neural network as a transferable feature extractor. In the adaptation stage, it fits a support vector regression model using the extracted features and testing data of the new environment. Simulation results demonstrate that compared to the state of the art meta learning method, our proposed algorithm reduces the complexities in both training and adaptation processes by more than an order of magnitude, while achieving better adaptation performance.

Type: Proceedings paper
Title: Fast Meta Learning for Adaptive Beamforming
Event: ICC 2021 - IEEE International Conference on Communication
ISBN-13: 9781728171227
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICC42927.2021.9500589
Publisher version: https://doi.org/10.1109/ICC42927.2021.9500589
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: Training, Deep learning, Adaptation models, Array signal processing, Simulation, Heuristic algorithms, Wireless networks
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/10136408
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