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 Hwang and Liu, Bo (2024) AI-Driven Design of a Quasi-Digitally-Coded Wideband Microstrip Patch Antenna Array. In: 2024 18th European Conference on Antennas and Propagation (EuCAP), 17-22 March 2024, Glasgow, United Kingdom.
|
Text
WURO_AI-Driven_Design_of_a_Quasi.pdf - Published Version Available under License Creative Commons Attribution. Download (758kB) | Preview |
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%.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keywords: | Training , Microstrip antenna arrays , Patch antennas , Impedance matching , Microstrip antennas , Machine learning , Microstrip, AI , Antenna Optimization , TR-SADEA |
| Divisions: | Applied Science, Computing and Engineering |
| Depositing User: | Hayley Dennis |
| Date Deposited: | 11 Jul 2024 11:10 |
| Last Modified: | 11 Jul 2024 11:10 |
| URI: | https://wrexham.repository.guildhe.ac.uk/id/eprint/18185 |
Actions (login required)
 |
Edit Item |