Support vector regression-enabled optimization strategy of dual circularly-polarized shaped-beam reflectarray with improved cross-polarization performance
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Reflectarray antennas
Dual circular polarization
Machine learning
Support vector regression
Surrogate models
Shaped-beam antenna
Crosspolar optimization
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IEEE
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Resumen:
This work presents the optimization of a dual circular-polarized (CP) shaped-beam reflectarray with improved performance. To that end, the design methodology leverages surrogate models based on support vector regression (SVR) of the electromagnetic response of the constituent unit cell for a direct layout optimization of the antenna. The dual CP capability is achieved using a Linear Polarization (LP) Jerusalem cross integrated with an LP-to-CP polarization converter. A full description of the reflectarray analysis in CP is given. We also provide a missing demonstration in the literature of the fact that the direct coefficients in CP shape the copolar pattern of the corresponding polarization. This is applied to the optimization of a dual CP reflectarray with an isoflux pattern, achieving a reduction of more than 9 dB in the crosspolar pattern.
This work presents the optimization of a dual circular-polarized (CP) shaped-beam reflectarray with improved performance. To that end, the design methodology leverages surrogate models based on support vector regression (SVR) of the electromagnetic response of the constituent unit cell for a direct layout optimization of the antenna. The dual CP capability is achieved using a Linear Polarization (LP) Jerusalem cross integrated with an LP-to-CP polarization converter. A full description of the reflectarray analysis in CP is given. We also provide a missing demonstration in the literature of the fact that the direct coefficients in CP shape the copolar pattern of the corresponding polarization. This is applied to the optimization of a dual CP reflectarray with an isoflux pattern, achieving a reduction of more than 9 dB in the crosspolar pattern.
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This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades under project IJC2018-035696-I; by the Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación within project ENHANCE-5G (PID2020-114172RB-C21 / AEI / 10.13039/501100011033); by Gobierno del Principado de Asturias under project AYUD/2021/51706; and by the Natural Sciences and Engineering Research Council of Canada (NSERC) at the University of Toronto.