DIMENSIONING OF FIXED FREQUENCY PATCH ANTENNAS BASED ON NEURAL NETWORKS

TASONA DOKO Jocelyn Tanguy; MATANGA Jacques; MALONG Yannick; ESSIBEN DIKOUNDOU Jean-François

doi:10.18535/ijsrm/v10i4.ec09

Abstract

During the last decades the race for innovation in communication systems has not ceased to evolve, which has led to important studies in the field of antennas, which today have very different forms depending on the applications such as: mobile telecommunications, television, radio, satellites, communicating systems, radar, remote sensing, radio astronomy [1]. Among the most used antenna families are the microstrip antennas (also called printed antennas or patch antennas) [2]. These antennas are characterized by: low manufacturing cost, mass production possible, linear and circular polarization, feed and matching networks manufactured simultaneously with the antenna.

The limitations of conventional neural modeling have been overcome by the introduction of neural networks based on electromagnetic knowledge. A neural network using effective parameters in conjunction with the GALERKIN function has been developed for modeling the resonant frequency of a rectangular antenna printed on a substrate [3]. Thus, we set up a rectangular PATCH antenna model on HFSS for a resonant frequency close to 6GHZ and extract the parameters S₁₁, Z₁₁, and VSWR, and also check the training errors on MATLAB by generating the error types on a 4-input model (Thickness, Length, Width and substrate type) and one (1) output (Resonant Frequency) that will allow to extract the final dimensions of the antennas thanks to this neural model.

Keywords

Rectangular patch antennamethod of momentsresonanceneural networksnetworks based on electromagnetic knowledge

References

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[refR-1] L'information et la communication dans l'entreprise », Bulletin des bibliothèques de France (BBF), 1993, n° 1, p. 76-76.Google Scholar ↗

[refR-2] A. Ngom, A. Diallo,K. Talla, A. Chiabo, I. Dioum, A.C. Beye, J.M. Ribero “Antenne à double polarisation et diagramme reconfigurable destinée aux « smallcells » pour la 5G”, 20 Journées Nationales Microondes- 16 – 19 Mai 2017, SAINT MALO, France ;Google Scholar ↗

[refR-3] P. Gahan, K. Mumford, D. Standeford, M. Sims, C. Viola, and J. Watson, “The 4G and 5G Spectrum Guide 2017,” The Mobile Spectrum Handbook, pp. 13–55, 2017.Google Scholar ↗

[refR-4] Y. CHUNYANG, G. DEYUAN, W. WENBING, “Nonuniform linear antenna array optimization –genetic algorithm approach “, ISAE’97 proceeding,1997.Google Scholar ↗

[refR-5] SCHEIR, G., HANSELAER, P., AND RYCKAERT, W. 2017. Pupillary light reflex, receptive field mechanism and correction for retinal position for the assessment of visual discomfort. Lighting Research & Technology, 1477153517737346.Google Scholar ↗

[refR-6] B. Cockburn, G. E. Karniadakis et C.-W. Shu (eds.), Discontinuous Galerkin methods. Theory, computation and applications, LectureNotes in Computational Science and Engineering, 11. Springer-Verlag, Berlin, 2000.Google Scholar ↗

[refR-7] E. Heidrich and W. Wiesbeck, “Wideband polarimetric rcs-antenna measurement,” in Seventh International Conference on (IEE) Antennas and Propagation, 1991. ICAP 91.,apr 1991, pp. 424 –427 vol.1.Google Scholar ↗

[refR-8] Baykan, Eda and Henzinger, Monika and Weber, Ingmar (2008). Web page language identification based on urls. Proceedings of the VLDB Endowment, 1 (1), 176–187.Google Scholar ↗

[refR-9] F. Rosenblatt, "The Perceptron: a probabilistic model for informationGoogle Scholar ↗

[refR-10] storage and organization in the brain," Psychological Review 65.Google Scholar ↗

[refR-11] Purely url-based topic classification. Proceedings of the 18th International Conference on World Wide Web. ACM, 1109–1110.Google Scholar ↗

[refR-12] Gashler, Michael S., and Stephen C. Ashmore. "Training Deep Fourier Neural Networks to Fit Time-Series Data." Intelligent Computing in Bioinformatics. Springer International Publishing, 2014. 48-55, « 1405.2262 » [archive], texte en accès libre, sur arXiv.Google Scholar ↗

[refR-13] Learning activation functions to improve deep neural networks. arXiv preprintarXiv :1412.6830.Google Scholar ↗

[refR-14] Battiti, Roberto (1989). Accelerated backpropagation learning : Two optimization methods. Complex Systems, 3 (4), 331–342.Google Scholar ↗

[refR-15] J.S. Hesthaven et T. Warburton, Nodal Discontinuous Galerkin Methods: Algorithms, Analysis, and Applications [archive]. Springer Texts in Applied Mathematics 54. Springer Verlag, New York, 2008.Google Scholar ↗

[refR-16] D.A. Di Pietro et A. Ern, Mathematical Aspects of Discontinuous Galerkin Methods [archive]. Mathématiques et Applications, Vol. 69, Springer-Verlag, Berlin, 2011.Google Scholar ↗

[refR-17] D.N. Arnold, F. Brezzi, B. Cockburn et L.D. Marini, Unified analysis of discontinuous Galerkin methods for elliptic problems, SIAM J. Numer. Anal. 39(5):1749-1779, 2002.Google Scholar ↗

[refR-18] M.E. Brachet, M. Meneguzzi, A. Vincent, H. Politano, and P.L. Sulem. Numerical evidence of smooth self-similar dynamics and possibility of subsequent collapse for three-dimensional ideal flows. Phys. Fluids, 4 :2845, 1992.Google Scholar ↗

[refR-19] M. Borrel and J. Ryan. The elastoplast discontinuous Galerkin (EDG) method for the Navier-Stokes equations. J. Comput. Phys., 231(1) :1–22, 2012.Google Scholar ↗

[refR-20] M.E. Brachet, D.I. Meiron, S.A. Orszag, BG Nickel, R.H. Morf, and U. Frisch. Smallscale structure of the Taylor-Green vortex. J. Fluid. Mech., 130(41) :1452, 1983.Google Scholar ↗

[refR-21] C. Brun, M. Petrovan Boiarciuc, M. Haberkorn, and P. Comte. Large eddy simulation of compressible channel flow. Theor. Comput. Fluid Dyn., 22(3) :189–212, 2008.Google Scholar ↗

[refR-22] Garg et al. « Microstrip antenna design handbook », Artech House, 2001.Google Scholar ↗

[refR-23] E. H. Newman, P. Bohley, C. H.Walter, "Two Methods for the Measurement of Antenna Efficiency", IEEE Trans. on Antennas and Propagation, vol AP-23, N°4, pp 457-461, July 1975.Google Scholar ↗

[refR-24] K. R. Carver, J. W. Mink, "Microstrip Antenna Technology", IEEE Trans. On Antennas and Propagation, vol AP-29, N°1, pp 2 -24, January 1981.Google Scholar ↗

[refR-25] I. J. Bahl, P. Bhartia, et S. S. Stuchly, ‘‘Design of microstrip antennas covered with a dielectric layer,’’ IEEE Trans. Antennas Propagat., vol. AP-30, pp. 314-318, Mar.1982.Google Scholar ↗

[refR-26] M. Amir, ‘‘Analyse d’une antenne microruban à patch supraconducteur,’’ Thèse de magister, Université de Batna, Fév. 2008.Google Scholar ↗

[refR-27] Robert Sève, Science de la couleur : Aspects physiques et perceptifs, Marseille, Chalagam, 2009, 374 p. (ISBN 978-2-9519607-5-6 et 2-9519607-5-1)Google Scholar ↗

[refR-28] ILHEM GHARBI, "Conception d’antennes reconfigurables en bandes millimétriques pour la 5G", SUPCOM Tunis, 2021Google Scholar ↗