Design of Zero Clearance SIW Endfire Antenna Array Using Machine Learning-Assisted Optimization

Jin Zhang, Mobayode Akinsolu, Bo Liu, Shuai Zhang*

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Abstrakt

In this paper, a substrate integrated waveguide (SIW) endfire antenna array with zero clearance is proposed for 5th 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 GHz 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 degree .
OriginalsprogEngelsk
TidsskriftI E E E Transactions on Antennas and Propagation
ISSN0018-926X
DOI
StatusE-pub ahead of print - 2022

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