Data-Driven Surrogate-Assisted Optimization of Metamaterial-Based Filtenna Using Deep Learning

Peyman Mahouti*, Mehmet Ali Belen, Ozlem Tari, Mehmet Ali Belen, Serdal Karahan, Slawomir Koziel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

In this work, a computationally efficient method based on data-driven surrogate models is proposed for the design optimization procedure of a Frequency Selective Surface (FSS)-based filtering antenna (Filtenna). A Filtenna acts as a module that simultaneously pre-filters unwanted signals, and enhances the desired signals at the operating frequency. However, due to a typically large number of design variables of FSS unit elements, and their complex interrelations affecting the scattering response, FSS optimization is a challenging task. Herein, a deep-learning-based algorithm, Modified-Multi-Layer-Perceptron (M2LP), is developed to render an accurate behavioral model of the unit cell. Subsequently, the M2LP model is applied to optimize FSS elements being parts of the Filtenna under design. The exemplary device operates at 5 GHz to 7 GHz band. The numerical results demonstrate that the presented approach allows for an almost 90% reduction of the computational cost of the optimization process as compared to direct EM-driven design. At the same time, physical measurements of the fabricated Filtenna prototype corroborate the relevance of the proposed methodology. One of the important advantages of our technique is that the unit cell model can be re-used to design FSS and Filtenna operating various operating bands without incurring any extra computational expenses.

Original languageEnglish
Article number1584
JournalElectronics (Switzerland)
Volume12
Issue number7
DOIs
Publication statusPublished - 28 Mar 2023

Bibliographical note

Funding Information:
The authors would like to thank Aktif Neser, for making CST Microwave Studio available. The research leading to these results has received funding from the Norway Grants 2014–2021 via the National Centre for Research and Development, grant NOR/POLNOR/HAPADS/0049/2019-00. This work was also supported in part by the Icelandic Centre for Research (RANNIS) Grant 217771.

Publisher Copyright:
© 2023 by the authors.

Other keywords

  • deep learning
  • filtering antenna
  • frequency selective surfaces
  • metamaterials
  • optimization

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