Model-Driven Beamforming Neural Networks.pdf (387.29 kB)

Model-driven beamforming neural networks

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journal contribution
posted on 19.11.2020, 12:27 by Wenchao Xia, Gan Zheng, Kai-Kit Wong, Hongbo Zhu
Beamforming is evidently a core technology in recent generations of mobile communication networks. Nevertheless, an iterative process is typically required to optimize the parameters, making it ill-placed for real-time implementation due to high complexity and computational delay. Heuristic solutions such as zero-forcing are simpler but at the expense of performance loss. Alternatively, DL is well understood to be a generalizing technique that can deliver promising results for a wide range of applications at much lower complexity if it is sufficiently trained. As a consequence, DL may present itself as an attractive solution to beamforming. To exploit DL, this article introduces general data-and model-driven BNNs, presents various possible learning strategies, and also discusses complexity reduction for DL-based BNNs. We also offer enhancement methods such as training-set augmentation and transfer learning in order to improve the generality of BNNs, accompanied by computer simulation results and testbed results showing the performance of such BNN solutions.

Funding

National Natural Science Foundation of China (Grant Nos. 61871446, 61801244, and 61801238)

Natural Science Foundation of Jiangsu Province (Grant No. BK20180754)

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

Engineering and Physical Sciences Research Council

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Leverhulme Trust Research Project Grant (Grant No. RPG-2017-129)

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Wireless Communications

Volume

27

Issue

1

Pages

68 - 75

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2020 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.

Publication date

2020-03-04

Copyright date

2020

ISSN

1536-1284

eISSN

1558-0687

Language

en

Depositor

Dr Gan Zheng. Deposit date: 16 November 2020

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