AI-Driven Design of a Quasi-Digitally-Coded Wideband Microstrip Patch Antenna Array

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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%.
Original languageEnglish
Title of host publication18th European Conference on Antennas and Propagation (EuCAP), Proceedings
Number of pages4
ISBN (Electronic)9788831299091
ISBN (Print)9798350394436
Publication statusPublished - 26 Apr 2024
Event18th European Conference on Antennas and Propagation - Glasgow, United Kingdom
Duration: 17 Mar 202422 Mar 2024
Conference number: 18


Conference18th European Conference on Antennas and Propagation
Abbreviated titleEuCAP 2024
Country/TerritoryUnited Kingdom
Internet address

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