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Shaped-Beam Reflectarray with a 15% Bandwidth Optimized Using Support Vector Regression

Author:
Rodríguez Prado, DanielUniovi authority; López Fernández, Jesús AlbertoUniovi authority; Arrebola Baena, ManuelUniovi authority; Rodríguez Pino, MarcosUniovi authority; Goussetis, George
Subject:

Machine learning

Support vector regression (SVR)

Wideban reflectarray antenna

Shaped-beam

Publication date:
2020-07
Editorial:

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.

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 (2020. Montreal)

URI:
http://hdl.handle.net/10651/55855
Patrocinado por:

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.

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