Design of Zero Clearance SIW Endfire Antenna Array Using Machine Learning-Assisted Optimization
Zhang, Jin, Akinsolu, Mobayode O., Liu, Bo and Zhang, Shuai (2021) Design of Zero Clearance SIW Endfire Antenna Array Using Machine Learning-Assisted Optimization. IEEE Transactions on Antennas and Propagation, 70 (5). ISSN 1558-2221
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WURO_SIW_Endfire_Antenna_Array.pdf - Accepted Version Available under License Creative Commons Attribution. Download (4MB) | Preview |
Abstract
In this communication, a substrate integrated waveguide (SIW) end-fire antenna array with zero clearance is proposed for fifth-generation (5G) mobile applications using machine learning-assisted optimization. In particular, a novel impedance matching architecture that involves three arbitrary pad-loading metallic vias is investigated and adopted for the antenna element. Due to the stringent design requirements, the locations and sizes of the vias and pads are obtained via a state-of-the-art machine learning assisted antenna design exploration method, parallel surrogate model-assisted hybrid differential evolution for antenna synthesis (PSADEA). Keeping a very low profile, the array optimized by PSADEA covers an operating frequency bandwidth from 36 to 40 GHz. The in-band total efficiency is generally better than 60% and the peak gain is above 5 dBi. The beam scanning range at 39 GHz covers from −20° to 35°.
| Item Type: | Article |
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| Keywords: | Antenna array, design exploration, optimization, substrate integrated waveguide (SIW) end-fire antenna, surrogate modeling. |
| Divisions: | Applied Science, Computing and Engineering |
| Depositing User: | Hayley Dennis |
| Date Deposited: | 11 Nov 2024 13:18 |
| Last Modified: | 11 Nov 2024 13:18 |
| URI: | https://wrexham.repository.guildhe.ac.uk/id/eprint/18239 |
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