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AI-Driven Design of a Quasi-digitally-coded Wideband Microstrip Patch Antenna Array

Akinsolu, Mobayode O.; Al-Yasir, Yasir I. A.; Hua, Qiang; See, Chan; Liu, Bo

Authors

Mobayode O. Akinsolu

Yasir I. A. Al-Yasir

Qiang Hua

Bo Liu



Abstract

Artificial intelligence (AI) is enabling the automated design of contemporary antennas for numerous applications. Specifically, the use of machine learning (ML)-assisted global optimization techniques for the efficient design of modern antennas is now fast becoming a popular method. In this work, we demonstrate for the first time, the ML-assisted global optimization of a high-dimensional non-uniform overlapping quasi-digitally coded microstrip patch antenna array using a new AI-driven antenna design technique, called TR-SADEA (the training cost-reduced surrogate model-assisted hybrid differential evolution for complex antenna optimization). The TR-SADEA-generated array showed very promising simulated frequency responses for potential wideband applications with a-10 dB impedance bandwidth of 5.75 GHz to 10 GHz, a minimum in-band realized gain of 5.82 dBi, and a minimum in-band total radiation efficiency of 87.84%.

Citation

Akinsolu, M. O., Al-Yasir, Y. I. A., Hua, Q., See, C., & Liu, B. (2024, March). AI-Driven Design of a Quasi-digitally-coded Wideband Microstrip Patch Antenna Array. Presented at EuCAP 2024, Glasgow, UK

Presentation Conference Type Conference Paper (published)
Conference Name EuCAP 2024
Start Date Mar 17, 2024
End Date Mar 22, 2024
Acceptance Date Dec 18, 2023
Online Publication Date Apr 26, 2024
Publication Date 2024
Deposit Date Dec 20, 2023
Publicly Available Date Apr 26, 2024
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
ISBN 9798350394436
DOI https://doi.org/10.23919/EuCAP60739.2024.10501667
Keywords AI, Antenna Optimization, and TR-SADEA
Public URL http://researchrepository.napier.ac.uk/Output/3434291
Related Public URLs https://www.eucap2024.org/

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