Efficient Shaped-Beam Reflectarray Design Using Machine Learning Techniques

Daniel R. Prado, Jesus A. Lopez-Fernandez, Manuel Arrebola, George Goussetis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


This papers introduces the use of machine learning techniques for an efficient design of shaped-beam reflectarrays considerably accelerating the overall process while providing accurate results. The technique is based on the use of Support Vector Machines (SVMs) for the characterization of the reflection coefficient matrix, which provides an efficient way for deriving the scattering parameters associated with the unit cell dimensions. In this way, the SVMs are used within the design process to obtain a reflectarray layout instead of a Full-Wave analysis tool based on Local Periodicity (FW-LP). The accuracy of the SVMs is assessed and the influence of the discretization of the angle of incidence is studied. Finally, a considerable acceleration is achieved with regard to the FW-LP and other works in the literature employing Artificial Neural Networks.

Original languageEnglish
Title of host publication2018 15th European Radar Conference (EuRAD)
Number of pages4
ISBN (Electronic)9782874870538
Publication statusPublished - 29 Nov 2018
Event15th European Radar Conference 2018 - Madrid, Spain
Duration: 26 Sept 201828 Sept 2018


Conference15th European Radar Conference 2018
Abbreviated titleEuRAD 2018


  • Machine Learning
  • reflectarray
  • Support Vector Machine (SVM)

ASJC Scopus subject areas

  • Signal Processing
  • Safety, Risk, Reliability and Quality
  • Instrumentation


Dive into the research topics of 'Efficient Shaped-Beam Reflectarray Design Using Machine Learning Techniques'. Together they form a unique fingerprint.

Cite this