Wideband Shaped-Beam Reflectarray Design Using Support Vector Regression Analysis

Daniel Rodriguez Prado, Jesus Alberto Lopez-Fernandez, Manuel Arrebola, Marcos Rodriguez Pino, George Goussetis

Research output: Contribution to journalArticle

3 Citations (Scopus)
1 Downloads (Pure)

Abstract

In recent years, machine learning techniques (MLTs) have been applied to accelerate the 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, multifrequency optimization of such large antennas has not been tackled with MLTs. In this letter, 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 the virtue of the proposed methodology, the minimum copolar gain in the coverage zone is improved more than 10 dB at the upper frequency while maintaining a computationally efficient design procedure.

Original languageEnglish
Pages (from-to)2287-2291
Number of pages5
JournalIEEE Antennas and Wireless Propagation Letters
Volume18
Issue number11
Early online date2 Aug 2019
DOIs
Publication statusPublished - Nov 2019

Keywords

  • Direct broadcast satellite (DBS)
  • Generalized intersection approach (IA)
  • Machine learning
  • Shaped-beam
  • Support vector regression (SVR)
  • Wideband reflectarray antenna

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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