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

doi:10.1109/AP-S/USNC-URSI47032.2022.9887077

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 proceeding › Conference contribution

7 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 language	English (US)
Title of host publication	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	499-500
Number of pages	2
ISBN (Electronic)	9781665496582
DOIs	https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887077
State	Published - 2022
Externally published	Yes
Event	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Denver, United States Duration: Jul 10 2022 → Jul 15 2022

Publication series

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

Conference

Conference	2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022
Country/Territory	United States
City	Denver
Period	7/10/22 → 7/15/22

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Signal Processing
Instrumentation

Keywords

Deep neural networks
antenna design
electromagnetic simulations
genetic algorithm
machine learning

Access to Document

10.1109/AP-S/USNC-URSI47032.2022.9887077

Cite this

Karahan, E. A., Gupta, A., Khankhoje, U. K., & Sengupta, K. (2022). Deep Learning based Modeling and Inverse Design for Arbitrary Planar Antenna Structures at RF and Millimeter-Wave. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings (pp. 499-500). (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887077

Karahan, Emir Ali ; Gupta, Aggraj ; Khankhoje, Uday K. et al. / Deep Learning based Modeling and Inverse Design for Arbitrary Planar Antenna Structures at RF and Millimeter-Wave. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 499-500 (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

@inproceedings{229e254aec704c678bc0398ec398adca,

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

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).",

keywords = "Deep neural networks, antenna design, electromagnetic simulations, genetic algorithm, machine learning",

author = "Karahan, {Emir Ali} and Aggraj Gupta and Khankhoje, {Uday K.} and Kaushik Sengupta",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 ; Conference date: 10-07-2022 Through 15-07-2022",

year = "2022",

doi = "10.1109/AP-S/USNC-URSI47032.2022.9887077",

language = "English (US)",

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

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "499--500",

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

address = "United States",

}

Karahan, EA, Gupta, A, Khankhoje, UK & Sengupta, K 2022, Deep Learning based Modeling and Inverse Design for Arbitrary Planar Antenna Structures at RF and Millimeter-Wave. in 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 499-500, 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022, Denver, United States, 7/10/22. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887077

Deep Learning based Modeling and Inverse Design for Arbitrary Planar Antenna Structures at RF and Millimeter-Wave. / Karahan, Emir Ali; Gupta, Aggraj; Khankhoje, Uday K. et al.
2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 499-500 (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Deep Learning based Modeling and Inverse Design for Arbitrary Planar Antenna Structures at RF and Millimeter-Wave

AU - Karahan, Emir Ali

AU - Gupta, Aggraj

AU - Khankhoje, Uday K.

AU - Sengupta, Kaushik

PY - 2022

Y1 - 2022

N2 - 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).

AB - 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).

KW - Deep neural networks

KW - antenna design

KW - electromagnetic simulations

KW - genetic algorithm

KW - machine learning

UR - http://www.scopus.com/inward/record.url?scp=85139750068&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85139750068&partnerID=8YFLogxK

U2 - 10.1109/AP-S/USNC-URSI47032.2022.9887077

DO - 10.1109/AP-S/USNC-URSI47032.2022.9887077

M3 - Conference contribution

AN - SCOPUS:85139750068

T3 - 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings

SP - 499

EP - 500

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

Y2 - 10 July 2022 through 15 July 2022

ER -

Karahan EA, Gupta A, Khankhoje UK, Sengupta K. Deep Learning based Modeling and Inverse Design for Arbitrary Planar Antenna Structures at RF and Millimeter-Wave. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 499-500. (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings). doi: 10.1109/AP-S/USNC-URSI47032.2022.9887077

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

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this