Nakmouche M.F.Derbal M.C.Allam A.M.M.A.Fawzy D.E.Shams S.I.Nedil M.Elsaadany M.2023-06-162023-06-1620219.78E+12https://doi.org/10.1109/APS/URSI47566.2021.9704795https://hdl.handle.net/20.500.14365/3502IEEE 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 -- 177295Machine 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.eninfo:eu-repo/semantics/closedAccessANNFrequency Selective SurfacesMachine LearningRidge Gap Waveguide (RGW)SuperstrateBandwidthLearning algorithmsMachine learningMicrowave antennasRidge waveguidesSlot antennasANNFrequency-selective surfacesGap waveguidesInternet applicationKu bandMachine-learningRidge gap waveguideSatellite internetSuperstratesWaveguide slot antennasFrequency selective surfacesConference Object10.1109/APS/URSI47566.2021.97047952-s2.0-85124043097