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Embedding model-based fast meta learning for downlink beamforming adaptation

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journal contribution
posted on 2022-01-28, 10:20 authored by Juping Zhang, Yi Yuan, Gan Zheng, Ioannis Krikidis, Kai-Kit Wong
This paper studies the fast adaptive beamforming for the multiuser multiple-input single-output downlink. Existing deep learning-based approaches assume that training and testing channels follow the same distribution which causes task mismatch, when the testing environment changes. Although meta learning can deal with the task mismatch, it relies on labelled data and incurs high complexity in the pre-training and fine tuning stages. We propose a simple yet effective adaptive framework to solve the mismatch issue, which trains an embedding model as a transferable feature extractor, followed by fitting the support vector regression. Compared to the existing meta learning algorithm, our method does not necessarily need labelled data in the pre-training and does not need fine-tuning of the pre-trained model in the adaptation. The effectiveness of the proposed method is verified through two well-known applications, i.e., the signal to interference plus noise ratio balancing problem and the sum rate maximization problem. Furthermore, we extend our proposed method to online scenarios in non-stationary environments. Simulation results demonstrate the advantages of the proposed algorithm in terms of both performance and complexity. The proposed framework can also be applied to general radio resource management problems.

Funding

Unlocking Potentials of MIMO Full-duplex Radios for Heterogeneous Networks (UPFRONT)

Engineering and Physical Sciences Research Council

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6G Mitola Radio: Cognitive Brain That Has Collective Intelligence

Engineering and Physical Sciences Research Council

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Leverhulme Trust Research Project under Grant RPG-2017-129

European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation under Project EXCELLENCE/0918/0377 (PRIME)

European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme under Grant 819819

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Transactions on Wireless Communications

Volume

21

Issue

1

Pages

149 - 162

Publisher

IEEE

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2021-06-25

Publication date

2021-07-09

Copyright date

2021

ISSN

1536-1276

eISSN

1558-2248

Language

  • en

Depositor

Prof Gan Zheng. Deposit date: 27 January 2022