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

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

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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 .
Original languageEnglish
JournalI E E E Transactions on Antennas and Propagation
Publication statusE-pub ahead of print - 2022


  • Antennas
  • Bandwidth
  • Design exploration
  • Dipole antennas
  • Load modeling
  • Loaded antennas
  • Mobile antennas
  • Resonant frequency
  • SIW endfire antenna
  • antenna array
  • optimization
  • surrogate modelling


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