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

Akinsolu, Mobayode O., Mistry, Keyur K., Liu, Bo, Lazaridis, Pavlos I. and Excell, Peter S (2020) Machine Learning-assisted Antenna Design optimization: A Review and the State-of-the-art. In: 14th European Conference on Antennas and Propagation (EuCAP), 15-20 March 2020, Copenhagen, Denmark..


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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 or Workshop Item (Paper)
Keywords: Antenna optimization, machine learning, surrogate model-based optimization.
Divisions: Applied Science, Computing and Engineering
Depositing User: Hayley Dennis
Date Deposited: 12 Apr 2021 11:58
Last Modified: 12 Apr 2021 11:59

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