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Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation

Yuan, Y; Zheng, G; Wong, KK; Ottersten, B; Luo, ZQ; (2020) Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation. IEEE Transactions on Wireless Communications 10.1109/TWC.2020.3035843. (In press). Green open access

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Abstract

IEEE This paper studies fast adaptive beamforming optimization for the signal-to-interference-plus-noise ratio balancing problem in a multiuser multiple-input single-output downlink system. Existing deep learning based approaches to predict beamforming rely on the assumption that the training and testing channels follow the same distribution which may not hold in practice. As a result, a trained model may lead to performance deterioration when the testing network environment changes. To deal with this task mismatch issue, we propose two offline adaptive algorithms based on deep transfer learning and meta-learning, which are able to achieve fast adaptation with the limited new labelled data when the testing wireless environment changes. Furthermore, we propose an online algorithm to enhance the adaptation capability of the offline meta algorithm in realistic non-stationary environments. Simulation results demonstrate that the proposed adaptive algorithms achieve much better performance than the direct deep learning algorithm without adaptation in new environments. The meta-learning algorithm outperforms the deep transfer learning algorithm and achieves near optimal performance. In addition, compared to the offline meta-learning algorithm, the proposed online meta-learning algorithm shows superior adaption performance in changing environments.

Type: Article
Title: Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TWC.2020.3035843
Publisher version: https://doi.org/10.1109/TWC.2020.3035843
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.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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/10119450
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