Mobayode O. Akinsolu
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
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%.
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 |
Deposit Date | Dec 20, 2023 |
Publisher | Institute of Electrical and Electronics Engineers |
Keywords | AI, Antenna Optimization, and TR-SADEA |
Public URL | http://researchrepository.napier.ac.uk/Output/3434291 |
Related Public URLs | https://www.eucap2024.org/ |
This file is under embargo due to copyright reasons.
Contact repository@napier.ac.uk to request a copy for personal use.
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