Machine Learning-assisted Antenna Design optimization: A Review and the State-of-the-art

Akinsolu, M. O., Mistry, K. K., Liu, B. , Lazaridis, P. I. and Excell, P. (2020) Machine Learning-assisted Antenna Design optimization: A Review and the State-of-the-art. In: 2020 14th European Conference on Antennas and Propagation (EuCAP), Copenhagen, Denmark, 15-20 Mar 2020, ISBN 9781728137124 (doi: 10.23919/EuCAP48036.2020.9135936)

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Abstract

Antenna design optimization continues to attract a lot of interest. This is mainly because traditional antenna design methodologies are exhaustive and have no guarantee of yielding successful outcomes due to the complexity of contemporary antennas in terms of topology and performance requirements. Though design automation via optimization complements conventional antenna design approaches, antenna design optimization still presents a number of challenges. The major challenges in antenna design optimization include the efficiency and optimization capability of available methods to address a broad scope of antenna design problems considering the growing stringent specifications of modern antennas. This paper presents a review of the most recent progress in antenna design optimization with a focus on methods which address the challenges of efficiency and optimization capability via machine learning techniques. The methods highlighted in this paper will likely have an impact on the future development of antennas for a multiplicity of applications.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Liu, Dr Bo
Authors: Akinsolu, M. O., Mistry, K. K., Liu, B., Lazaridis, P. I., and Excell, P.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:9781728137124
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in 2020 14th European Conference on Antennas and Propagation (EuCAP)
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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