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01-02-2025

Unraveling the resonant frequency of H-shaped microstrip antennas using a deep learning approach

Authors: Akram Bediaf, Sami Bedra, Djemai Arar, Mohamed Bedra

Published in: Journal of Computational Electronics | Issue 1/2025

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Abstract

This paper introduces a novel physics-informed learning approach that combines principles from physics with deep learning techniques to optimize the simulation process of microstrip antennas. These deep learning-based approaches are preferable because traditional full-wave models used in antenna design are computationally intensive and require significant memory due to their reliance on iterative algorithms, leading to exponential increases in resource demands as input parameters grow. In contrast, the proposed deep learning method requires significant computational resources only during training, with a constant time complexity of O(1) during deployment. This results in much faster modeling, allowing a broader range of antenna configurations to be processed more quickly, thereby improving the efficiency of the design workflow. Unlike conventional deep learning methods that rely solely on data, our approach leverages the underlying physical laws governing antenna behavior, particularly beneficial when labeled data is scarce or difficult to obtain. We propose a bias observational physics-informed learning technique by integrating physical laws into the loss function, which includes two components: Neuron Loss, the standard MSE measuring prediction accuracy against actual data, and Physics Loss, which penalizes deviations from physical laws as represented by a cavity model. The total loss combines these two, with higher physics loss indicating poorer alignment with physical principles and lower physics loss suggesting better adherence to them. This approach refines predictions by balancing data fidelity with physical constraint, wherein the dataset is sourced from simulations and real-world measurements. This strategy ensures model uncertainty and broad generalization capabilities. Computational efficiency is a key consideration, with our approach implemented on low-specification hardware, emphasizing optimal resource and power consumption. The H-shaped microstrip antennas (HMAs), known for its wide and dual-band properties, serves as the target antenna for our study. We employ three sequential models’ recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU)—integrated with a cavity model-driven resonance frequency representation to maintain the resonance mode TM10 at prediction. Comparative analysis of these models encompasses execution time, prediction convergence, loss reduction, prediction score (R2), as well as memory and CPU usage. This research contributes four main sections elucidating the methodology, experimental setup, and results analysis, underscoring the efficacy of our deep learning approach in antenna optimization.

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Literature
1.
go back to reference Bedra, S., Bedra, R., Benkouda, S., Fortaki, T.: Study of an inverted rectangular patch printed on anisotropic substrates. IETE J. Res. 68(2), 1056–1063 (2022)CrossRef Bedra, S., Bedra, R., Benkouda, S., Fortaki, T.: Study of an inverted rectangular patch printed on anisotropic substrates. IETE J. Res. 68(2), 1056–1063 (2022)CrossRef
2.
go back to reference Bediaf, A. et al.: "Unlocking enhanced bandwidth for 5G microstrip antennas: a comparative analysis of substrate anisotropy and thickness manipulation techniques," International conference on electrical engineering and advanced technology (ICEEAT), vol. 1: IEEE, pp. 1–4, 2023. Bediaf, A. et al.: "Unlocking enhanced bandwidth for 5G microstrip antennas: a comparative analysis of substrate anisotropy and thickness manipulation techniques," International conference on electrical engineering and advanced technology (ICEEAT), vol. 1: IEEE, pp. 1–4, 2023.
3.
go back to reference Bedra, S., Fortaki, T.: Rigorous full-wave analysis of rectangular microstrip patch antenna on suspended and composite substrates. Wirel. Pers. Commun. 78, 1455–1463 (2014)CrossRefMATH Bedra, S., Fortaki, T.: Rigorous full-wave analysis of rectangular microstrip patch antenna on suspended and composite substrates. Wirel. Pers. Commun. 78, 1455–1463 (2014)CrossRefMATH
4.
go back to reference Bedra, S., Bedra, R., Benkouda, S., Fortaki, T.: Superstrate loading effects on the resonant characteristics of high Tc superconducting circular patch printed on anisotropic materials. Phys. C Superconduct. Appl. 543, 1–7 (2017)CrossRef Bedra, S., Bedra, R., Benkouda, S., Fortaki, T.: Superstrate loading effects on the resonant characteristics of high Tc superconducting circular patch printed on anisotropic materials. Phys. C Superconduct. Appl. 543, 1–7 (2017)CrossRef
5.
go back to reference Han, S., Tian, Y., Ding, W., Li, P.: Resonant frequency modeling of microstrip antenna based on deep kernel learning. IEEE Access 9, 39067–39076 (2021)CrossRefMATH Han, S., Tian, Y., Ding, W., Li, P.: Resonant frequency modeling of microstrip antenna based on deep kernel learning. IEEE Access 9, 39067–39076 (2021)CrossRefMATH
6.
go back to reference Wang, B. Z., Xiao, L. Y., and Shao, W.: “Advanced neural networks for electromagnetic modeling and design,” Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning, pp. 141–196, 2023 Wang, B. Z., Xiao, L. Y., and Shao, W.: “Advanced neural networks for electromagnetic modeling and design,” Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning, pp. 141–196, 2023
7.
go back to reference Antonelo, E.A., Camponogara, E., Seman, L.O., Jordanou, J.P., de Souza, E.R., Hübner, J.F.: Physics-informed neural nets for control of dynamical systems. Neurocomputing. 28(579), 127419 (2024)CrossRef Antonelo, E.A., Camponogara, E., Seman, L.O., Jordanou, J.P., de Souza, E.R., Hübner, J.F.: Physics-informed neural nets for control of dynamical systems. Neurocomputing. 28(579), 127419 (2024)CrossRef
8.
go back to reference Ustun, D., Toktas, F.: Surrogate-based computational analysis and design for H-shaped microstrip antenna. J. Electromag. Waves Appl. 35(1), 71–82 (2021)CrossRefMATH Ustun, D., Toktas, F.: Surrogate-based computational analysis and design for H-shaped microstrip antenna. J. Electromag. Waves Appl. 35(1), 71–82 (2021)CrossRefMATH
9.
go back to reference Bedra, S., Benkouda, S., Amir, M., Fortaki, T.: Resonant frequency of tunable microstrip ring antenna printed on isotropic or uniaxially anisotropic substrate. Adv. Electromagnet. 2(2), 6–9 (2013)CrossRef Bedra, S., Benkouda, S., Amir, M., Fortaki, T.: Resonant frequency of tunable microstrip ring antenna printed on isotropic or uniaxially anisotropic substrate. Adv. Electromagnet. 2(2), 6–9 (2013)CrossRef
10.
go back to reference James, J. R., Hall, P. S., and Wood, C.: Microstrip Antenna: Theory and Design. Iet, 1986 James, J. R., Hall, P. S., and Wood, C.: Microstrip Antenna: Theory and Design. Iet, 1986
11.
go back to reference Schneider, G.A.: Influence of electric field and mechanical stresses on the fracture of ferroelectrics. Annu. Rev. Mater. Res. 37, 491–538 (2007)CrossRefMATH Schneider, G.A.: Influence of electric field and mechanical stresses on the fracture of ferroelectrics. Annu. Rev. Mater. Res. 37, 491–538 (2007)CrossRefMATH
12.
go back to reference Wang, Z., Li, X., Fang, S., Liu, Y.: An accurate edge extension formula for calculating resonant frequency of electrically thin and thick rectangular patch antennas with and without air gaps. IEEE Access 4, 2388–2397 (2016)CrossRefMATH Wang, Z., Li, X., Fang, S., Liu, Y.: An accurate edge extension formula for calculating resonant frequency of electrically thin and thick rectangular patch antennas with and without air gaps. IEEE Access 4, 2388–2397 (2016)CrossRefMATH
13.
go back to reference Garg, R.: Microstrip Antenna Design Handbook. Artech house, New York (2001)MATH Garg, R.: Microstrip Antenna Design Handbook. Artech house, New York (2001)MATH
14.
go back to reference Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31(7), 1235–1270 (2019)MathSciNetCrossRefMATH Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31(7), 1235–1270 (2019)MathSciNetCrossRefMATH
15.
go back to reference Yang, B., Lu, P., Du, C., Cao, F.: A GRU network framework towards fault-tolerant control for flight vehicles based on a gain-scheduled approach. Aerospace Sci. Technol. 1(146), 108954 (2024)CrossRefMATH Yang, B., Lu, P., Du, C., Cao, F.: A GRU network framework towards fault-tolerant control for flight vehicles based on a gain-scheduled approach. Aerospace Sci. Technol. 1(146), 108954 (2024)CrossRefMATH
16.
go back to reference Shi, X., Chen, S., Wang, Q., Lu, Y., Ren, S., Huang, J.: Mechanical framework for geopolymer gels construction: an optimized LSTM technique to predict compressive strength of fly ash-based geopolymer gels concrete. Gels 10(2), 148 (2024)CrossRef Shi, X., Chen, S., Wang, Q., Lu, Y., Ren, S., Huang, J.: Mechanical framework for geopolymer gels construction: an optimized LSTM technique to predict compressive strength of fly ash-based geopolymer gels concrete. Gels 10(2), 148 (2024)CrossRef
17.
go back to reference Barkat, L., Bedra, S., Fortaki, T., Bedra, R.: Neurospectral computation for the resonant characteristics of microstrip patch antenna printed on uniaxially anisotropic substrates. Int. J. Microw. Wirel. Technol. 9(3), 613–620 (2017)CrossRef Barkat, L., Bedra, S., Fortaki, T., Bedra, R.: Neurospectral computation for the resonant characteristics of microstrip patch antenna printed on uniaxially anisotropic substrates. Int. J. Microw. Wirel. Technol. 9(3), 613–620 (2017)CrossRef
18.
go back to reference Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019)MathSciNetCrossRefMATH Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019)MathSciNetCrossRefMATH
19.
go back to reference Sun, L., Gao, H., Pan, S., Wang, J.-X.: Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data. Comput. Methods Appl. Mech. Eng. 361, 112732 (2020)MathSciNetCrossRefMATH Sun, L., Gao, H., Pan, S., Wang, J.-X.: Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data. Comput. Methods Appl. Mech. Eng. 361, 112732 (2020)MathSciNetCrossRefMATH
20.
go back to reference Zhu, Y., Zabaras, N., Koutsourelakis, P.-S., Perdikaris, P.: Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. J. Comput. Phys. 394, 56–81 (2019)MathSciNetCrossRefMATH Zhu, Y., Zabaras, N., Koutsourelakis, P.-S., Perdikaris, P.: Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. J. Comput. Phys. 394, 56–81 (2019)MathSciNetCrossRefMATH
21.
go back to reference Baldan, M., Di Barba, P., Lowther, D.A.: Physics-informed neural networks for inverse electromagnetic problems. IEEE Trans. Magn. 59(5), 1–5 (2023)CrossRefMATH Baldan, M., Di Barba, P., Lowther, D.A.: Physics-informed neural networks for inverse electromagnetic problems. IEEE Trans. Magn. 59(5), 1–5 (2023)CrossRefMATH
22.
go back to reference Ahmed, S.F., et al.: Deep learning modelling techniques: current progress, applications, advantages, and challenges. Artif. Intell. Rev. 56(11), 13521–13617 (2023)CrossRef Ahmed, S.F., et al.: Deep learning modelling techniques: current progress, applications, advantages, and challenges. Artif. Intell. Rev. 56(11), 13521–13617 (2023)CrossRef
23.
go back to reference Raymond, S. J., and Camarillo, D. B.: “Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems,” arXiv e-prints, pp. arXiv-2105, 2021. Raymond, S. J., and Camarillo, D. B.: “Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems,” arXiv e-prints, pp. arXiv-2105, 2021.
24.
go back to reference Koutnik, J., Greff, K., Gomez, F., and Schmidhuber, J.: “A clockwork rnn,” in International conference on machine learning, PMLR, pp. 1863–1871, 2014 Koutnik, J., Greff, K., Gomez, F., and Schmidhuber, J.: “A clockwork rnn,” in International conference on machine learning, PMLR, pp. 1863–1871, 2014
25.
go back to reference Mim, T.R., et al.: GRU-INC: an inception-attention based approach using GRU for human activity recognition. Expert Syst. Appl. 216, 119419 (2023)CrossRefMATH Mim, T.R., et al.: GRU-INC: an inception-attention based approach using GRU for human activity recognition. Expert Syst. Appl. 216, 119419 (2023)CrossRefMATH
26.
go back to reference Lindemann, B., Maschler, B., Sahlab, N., Weyrich, M.: A survey on anomaly detection for technical systems using LSTM networks. Comput. Ind. 131, 103498 (2021)CrossRef Lindemann, B., Maschler, B., Sahlab, N., Weyrich, M.: A survey on anomaly detection for technical systems using LSTM networks. Comput. Ind. 131, 103498 (2021)CrossRef
Metadata
Title
Unraveling the resonant frequency of H-shaped microstrip antennas using a deep learning approach
Authors
Akram Bediaf
Sami Bedra
Djemai Arar
Mohamed Bedra
Publication date
01-02-2025
Publisher
Springer US
Published in
Journal of Computational Electronics / Issue 1/2025
Print ISSN: 1569-8025
Electronic ISSN: 1572-8137
DOI
https://doi.org/10.1007/s10825-024-02270-6