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

Mobayode O. Akinsolu, Keyur K. Mistry, Bo Liu, Pavlos I. Lazaridis, Peter Excell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication14th European Conference on Antennas and Propagation, EuCAP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9788831299008
ISBN (Print)9781728137124
DOIs
Publication statusPublished - 8 Jul 2020
Event14th European Conference on Antennas and Propagation - Copenhagen, Denmark
Duration: 15 Mar 202020 Mar 2020
Conference number: 14
https://www.eucap2020.org/

Conference

Conference14th European Conference on Antennas and Propagation
Abbreviated titleEuCAP 2020
CountryDenmark
CityCopenhagen
Period15/03/2020/03/20
Internet address

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