Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3502
Title: Machine Learning Based Design of Ku Band Ridge Gap Waveguide Slot Antenna Loaded with FSS for Satellite Internet Applications
Authors: Nakmouche M.F.
Derbal M.C.
Allam A.M.M.A.
Fawzy D.E.
Shams S.I.
Nedil M.
Elsaadany M.
Keywords: ANN
Frequency Selective Surfaces
Machine Learning
Ridge Gap Waveguide (RGW)
Superstrate
Bandwidth
Learning algorithms
Machine learning
Microwave antennas
Ridge waveguides
Slot antennas
ANN
Frequency-selective surfaces
Gap waveguides
Internet application
Ku band
Machine-learning
Ridge gap waveguide
Satellite internet
Superstrates
Waveguide slot antennas
Frequency selective surfaces
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: Machine learning has been used in this work for the development of a Ku band Ridge Gap Waveguide (RGW) slot antenna loaded with an FSS superstrate for satellite internet applications. The structure operates from 13.25 to 14.75 GHz with a gain beyond 10 dB using FSS superstrate loading. The developed machine learning model aims to predict the optimal length and width of the radiated slot, where both the Fractional Bandwidth (FBW) and the resonance frequency are considered objective parameters. The simulated results and the anticipated results through the machine learning algorithm are in good agreement. © 2021 IEEE.
Description: IEEE Antennas and Propagation Society (AP-S);US National Committee (USNC) of the International Union of Radio Science (URSI)
2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 -- 4 December 2021 through 10 December 2021 -- 177295
URI: https://doi.org/10.1109/APS/URSI47566.2021.9704795
https://hdl.handle.net/20.500.14365/3502
ISBN: 9.78173E+12
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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