Deep Learning based Modeling and Inverse Design for Arbitrary Planar Antenna Structures at RF and Millimeter-Wave

Emir Ali Karahan, Aggraj Gupta, Uday K. Khankhoje, Kaushik Sengupta

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

1 Scopus citations

Abstract

In this paper, we introduce inverse design of nearly arbitrary planar antenna structures with a deep convolutional neural network (CNN) modeling that allows rapid and accurate prediction of antenna performance (scattering parameters and radiation patterns). Quite distinct from prior efforts of ML-based antennas with fixed template geometries and finite degrees of freedom, this approach of generalizing to arbitrary planar structures opens up a new design space for antenna structures with properties beyond what can be achieved with antennas optimized from a finite library. By eliminating complex time consuming electromagnetic simulations with an ML-based approach, we propose an inverse design with evolutionary algorithms that allows a much larger search space than classical genetic algorithm based approaches. We demonstrate this methodology with simulation and measurement results of inverse designed compact, broadband and multi-band planar antennas operating at RF (2-5 GHz) and mmWave (20-40 GHz).

Original languageEnglish (US)
Title of host publication2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages499-500
Number of pages2
ISBN (Electronic)9781665496582
DOIs
StatePublished - 2022
Event2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Denver, United States
Duration: Jul 10 2022Jul 15 2022

Publication series

Name2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings

Conference

Conference2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022
Country/TerritoryUnited States
CityDenver
Period7/10/227/15/22

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation

Keywords

  • antenna design
  • Deep neural networks
  • electromagnetic simulations
  • genetic algorithm
  • machine learning

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