Machine Learning-Assisted Optimization of a Metasurface-Based Directly Modulating Antenna

Henthorn, Stephen, Akinsolu, Mobayode O., Lee Ford, Kenneth and O’Farrell, Timothy (2022) Machine Learning-Assisted Optimization of a Metasurface-Based Directly Modulating Antenna. In: 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), 10-15 July 2022, Denver, Colorado, USA.

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Official URL: https://ieeexplore.ieee.org/document/9886083

Abstract

A directly modulating antenna using metasurfaces is optimized using the surrogate model assisted differential evolution for antenna synthesis (SADEA) method and simulated. Metasurface modulation holds promise as an energy efficiency transmitter technology, but suffers from modulation distortion and many differing parameters, making achieving good designs difficult. The algorithm used here, SADEA, obtained a design that shows improvement over conventional design techniques, producing amplitude variation of 1.8 dB over 360° and an average efficiency of 65%, up from 50% obtained by the standard model.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Machine learning algorithms , Radio transmitters , Modulation , Metasurfaces , Physical layer , Distortion , Energy efficiency Index Terms Direct Modulation , Alternative Models , Standard Model , Differential Evolution , Conventiona
Divisions:	Applied Science, Computing and Engineering
Depositing User:	Hayley Dennis
Date Deposited:	11 Jul 2024 12:11
Last Modified:	11 Jul 2024 12:16
URI:	https://wrexham.repository.guildhe.ac.uk/id/eprint/18190

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