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Título: Wideband Shaped-Beam Reflectarray Design Using Support Vector Regression Analysis
Autores: Rodriguez Prado, Daniel
López Fernádez, Jesús Alberto
Arrebola Baena, Manuel
Rodríguez Pino, Marcos
Goussetis, George
Palabras Claves: machine learning
support vector regression
wideband reflectarray antenna
direct broadcast satellite
generalized Intersection Approach
Fecha Edición: Nov-2019
Editor: IEEE
Cita Bibliográfica: D. R. Prado, J. A. López-Fernández, M. Arrebola, M. R. Pino, G. Goussetis, "Wideband Shaped-Beam Reflectarray Design Using a Machine Learning Machine", IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 11, pp. 2287-2291, Nov. 2019
Resumen: In recent years, machine learning techniques (MLTs) have been applied to accelerate analysis and design of electromagnetic devices. Algorithms such as artificial neural networks or support vector machines for regression (SVRs) have been proposed for the design of large reflectarrays for space applications at a single frequency. However, multi-frequency optimization of such large antennas has not been tackled with MLTs. In this letter and for the first time, we propose a technique based on the use of SVR analysis to obtain the reflection coefficients to accelerate the design of a very large shaped-beam reflectarray for direct broadcast satellite in a 15% bandwidth. An in-house method of moments based on local periodicity is employed to generate samples to train the SVRs for each considered frequency. Then, the surrogate model is used for a design at central frequency, which is used as starting point for a wideband design procedure that is accelerated more than an order of magnitude without a significant loss of accuracy. It is shown that, by virtue of the proposed methodology, the minimum copolar gain in the coverage zone is improved more than 10dB at the upper frequency while maintaining a computationally efficient design procedure.
ISSN: 1536-1225
Aparece en las Colecciones:Open Access DRIVERset

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