Design of zero clearance SIW endfire antenna array using machine learning-assisted optimization

Zhang, J., Akinsolu, M. O., Liu, B. and Zhang, S. (2022) Design of zero clearance SIW endfire antenna array using machine learning-assisted optimization. IEEE Transactions on Antennas and Propagation, 70(5), pp. 3858-3863. (doi: 10.1109/TAP.2021.3137500)

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Abstract

In this paper, a substrate integrated waveguide (SIW) endfire antenna array with zero clearance is proposed for 5th generation (5G) mobile applications using machine learning-assisted optimization. In particular, a novel impedance matching architecture that involves three arbitrary pad-loading metallic vias is investigated and adopted for the antenna element. Due to the stringent design requirements, the locations and sizes of the vias and pads are obtained via a state-of-the-art machine learning assisted antenna design exploration method, parallel surrogate model-assisted hybrid differential evolution for antenna synthesis (PSADEA). Keeping a very low profile, the array optimized by PSADEA covers an operating frequency bandwidth from 36 GHz to 40 GHz. The in-band total efficiency is generally better than 60% and the peak gain is above 5 dBi. The beam scanning range at 39 GHz covers from -20° to 35°.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Liu, Dr Bo
Authors: Zhang, J., Akinsolu, M. O., Liu, B., and Zhang, S.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Transactions on Antennas and Propagation
Publisher:IEEE
ISSN:0018-926X
ISSN (Online):1558-2221
Published Online:29 December 2021
Copyright Holders:Copyright © 2021 IEEE
First Published:First published in IEEE Transactions on Antennas and Propagation 70(5): 3858-3863
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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