Machine learning based tandem network approach for antenna design

Aggraj Gupta; Chandan Bhat; Emir Karahan; Kaushik Sengupta; Uday K. Khankhoje

doi:10.1109/AP-S/USNC-URSI47032.2022.9886551

Machine learning based tandem network approach for antenna design

Aggraj Gupta, Chandan Bhat, Emir Karahan, Kaushik Sengupta, Uday K. Khankhoje

Electrical and Computer Engineering

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

1 Scopus citations

Abstract

In this paper, we introduce novel machine learning based techniques to design multi-band microstrip antennas as per user specifications over a broad range of frequencies. The approach involves the design and training of a neural network for approximating the electromagnetic simulations of antennas, the so-called 'forward' problem. Here, the antenna is parameterized in terms of a checker-board pattern of metallic sub-patches. Additionally, a second 'tandem' neural network is also designed, which takes the user specification of a desired return-loss spectrum and returns an antenna structure. We explore the various machine learning innovations that are required in order for this approach to succeed. Our approach makes way for rapid designs of multi-band antennas, which is otherwise known to be a tedious task requiring vast domain knowledge.

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	489-490
Number of pages	2
ISBN (Electronic)	9781665496582
DOIs	https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9886551
State	Published - 2022
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

Access to Document

10.1109/AP-S/USNC-URSI47032.2022.9886551

Cite this

Gupta, A., Bhat, C., Karahan, E., Sengupta, K., & Khankhoje, U. K. (2022). Machine learning based tandem network approach for antenna design. In 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings (pp. 489-490). (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-URSI47032.2022.9886551

Gupta, Aggraj ; Bhat, Chandan ; Karahan, Emir et al. / Machine learning based tandem network approach for antenna design. 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. 489-490 (2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings).

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title = "Machine learning based tandem network approach for antenna design",

abstract = "In this paper, we introduce novel machine learning based techniques to design multi-band microstrip antennas as per user specifications over a broad range of frequencies. The approach involves the design and training of a neural network for approximating the electromagnetic simulations of antennas, the so-called 'forward' problem. Here, the antenna is parameterized in terms of a checker-board pattern of metallic sub-patches. Additionally, a second 'tandem' neural network is also designed, which takes the user specification of a desired return-loss spectrum and returns an antenna structure. We explore the various machine learning innovations that are required in order for this approach to succeed. Our approach makes way for rapid designs of multi-band antennas, which is otherwise known to be a tedious task requiring vast domain knowledge.",

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

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",

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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.",

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Gupta, A, Bhat, C, Karahan, E, Sengupta, K & Khankhoje, UK 2022, Machine learning based tandem network approach for antenna design. 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. 489-490, 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-URSI47032.2022.9886551

Machine learning based tandem network approach for antenna design. / Gupta, Aggraj; Bhat, Chandan; Karahan, Emir 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. 489-490 (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

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N2 - In this paper, we introduce novel machine learning based techniques to design multi-band microstrip antennas as per user specifications over a broad range of frequencies. The approach involves the design and training of a neural network for approximating the electromagnetic simulations of antennas, the so-called 'forward' problem. Here, the antenna is parameterized in terms of a checker-board pattern of metallic sub-patches. Additionally, a second 'tandem' neural network is also designed, which takes the user specification of a desired return-loss spectrum and returns an antenna structure. We explore the various machine learning innovations that are required in order for this approach to succeed. Our approach makes way for rapid designs of multi-band antennas, which is otherwise known to be a tedious task requiring vast domain knowledge.

AB - In this paper, we introduce novel machine learning based techniques to design multi-band microstrip antennas as per user specifications over a broad range of frequencies. The approach involves the design and training of a neural network for approximating the electromagnetic simulations of antennas, the so-called 'forward' problem. Here, the antenna is parameterized in terms of a checker-board pattern of metallic sub-patches. Additionally, a second 'tandem' neural network is also designed, which takes the user specification of a desired return-loss spectrum and returns an antenna structure. We explore the various machine learning innovations that are required in order for this approach to succeed. Our approach makes way for rapid designs of multi-band antennas, which is otherwise known to be a tedious task requiring vast domain knowledge.

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Gupta A, Bhat C, Karahan E, Sengupta K, Khankhoje UK. Machine learning based tandem network approach for antenna design. 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. 489-490. (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-URSI47032.2022.9886551

Machine learning based tandem network approach for antenna design

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