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

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/