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Article


Title

Modeling of the 5G-Band Patch Antennas Using ANNs under the Uncertainty of the Geometrical Design Parameters Associated with the Manufacturing Process

Authors

[ 1 ] Instytut Telekomunikacji Multimedialnej, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Published in

Algorithms

Journal year: 2022 | Journal volume: vol. 15 | Journal number: iss. 1

Article type

scientific article

Publication language

english

Keywords
EN
  • artificial neural network
  • polynomial chaos expansion
  • patch antenna
  • frequency difference time-domain
  • random variables
Abstract

EN In the paper, the author deals with modeling the stochastic behavior of ordinary patch antennas in terms of the mean and standard deviation of their reflection coefficient |S11| under the geometrical uncertainty associated with their manufacturing process. The Artificial Neural Network is used to model the stochastic reflection coefficient of the antennas. The Polynomial Chaos Expansion and FDTD computations are used to obtain the training and testing data for the Artificial Neural Network. For the first time, the author uses his analytical transformations to reduce the required number of highly time-consuming FDTD simulations for a given set of nominal values of the design parameters of the ordinary patch antenna. An analysis is performed for n257 and n258 frequency bands (24.5–28.7 GHz). The probability distributions of the design parameters are extracted from the measurement results obtained for a series of manufactured patch antenna arrays for three different frequencies in the C, X, and Ka bands. Patch antennas are chosen as the subject of the scientific analysis in this paper because of the popularity of the patch antennas in the scientific literature concerning antennas, as well as because of a simple form of these antennas that is reflected in the time required for computation of training and testing data for the Artificial Neural Network.

Pages (from - to)

0007-1 - 0007-14

DOI

10.3390/a15010007

URL

https://www.mdpi.com/1999-4893/15/1/7/htm

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

40.0

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