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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/55855

Title: Shaped-Beam Reflectarray with a 15% Bandwidth Optimized Using Support Vector Regression
Author(s): Rodríguez Prado, Daniel
López Fernández, Jesús Alberto
Arrebola Baena, Manuel
Rodríguez Pino, Marcos
Goussetis, George
Keywords: Machine learning
Support vector regression (SVR)
Wideban reflectarray antenna
Shaped-beam
Issue date: Jul-2020
Publisher: IEEE
Abstract: Support Vector Regression (SVR) is employed in a wideband, copolar reflectarray optimization to achieve a 15% bandwidth. The reflectarray is square and 1 meter wide. A European coverage with a minimum gain requirement of 28 dBi has been chosen. After the optimization, the minimum copolar gain in the coverage zone is improved more than 10 dB at the upper frequency while maintaining an accurate and computationally efficient design procedure
Description: IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting (Montreal. 2020)
URI: http://hdl.handle.net/10651/55855
Sponsored: This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades under project TEC2017-86619-R (ARTEINE); by the Ministerio de Economía, Industria y Competitividad under project TEC2016-75103-C2-1-R (MYRADA); by the Gobierno del Principado de Asturias/FEDER under Project GRUPIN-IDI/2018/000191
Project id.: TEC2017-86619-R (ARTEINE)
TEC2016-75103-C2-1-R (MYRADA)
GRUPIN-IDI/2018/000191
Appears in Collections:Ponencias, Discursos y Conferencias
Ingeniería Eléctrica, Electrónica, de Computadores y Sistemas
Investigaciones y Documentos OpenAIRE

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