Rapid Design of 3D Reflectarray Antennas by Inverse Surrogate Modeling and Regularization

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Koziel S., Belen M. A., ÇALIŞKAN A., MAHOUTİ P.

IEEE Access, vol.11, pp.24175-24184, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11
  • Publication Date: 2023
  • Doi Number: 10.1109/access.2023.3254204
  • Journal Name: IEEE Access
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.24175-24184
  • Keywords: Reflection, Computational modeling, Costs, Optimization, Inverse problems, Reflector antennas, Computational efficiency, Antenna design, reflectarrays, surrogate modeling, inverse Modeling, EM-driven design, regularization
  • Yıldız Technical University Affiliated: Yes


Reflectarrays (RAs) exhibit important advantages over conventional antenna arrays, especially in terms of realizing pencil-beam patterns without the employment of the feeding networks. Unfortunately, microstrip RA implementations feature narrow bandwidths, and are severely affected by losses. A considerably improved performance can be achieved for RAs involving grounded dielectric layers, which are also easy to manufacture using 3D printing technology. Regardless of the implementation details, a practical bottleneck of RA design is the necessity of independent adjustment of a large number of unit cells, which has to be carried out using full-wave electromagnetic (EM) simulation models to ensure reliability. The associated computational costs are extraordinary. A practical workaround is the incorporation of surrogate modeling methods; however, a construction of accurate metamodel requires a large number of training data samples. This letter introduces an alternative RA design approach, where the unit cells are adjusted using an inverse surrogate model established with a small number of anchor points, pre-optimized for the reference reflection phases. To ensure solution uniqueness, the anchor point optimization involves regularization, here, based on the minimum-volume condition for the unit cell. The presented approach reduces the computational cost of RA design to a few dozens of EM analyses of the cell. Several demonstration examples are provided, along with an experimental validation of the selected RA realization.