Resonance Frequency Prediction of Aperture Coupled Microstrip Antenna Based on Artificial Neural Network

DOI

10.2991/aisr.k.220201.031How to use a DOI?

Keywords

Aperture; Coupled; Microstrip; Patch; Antenna; Radar; Software; Model; Neural; Network; Learning; Levenberg; Marquardt; Resonant; Frequency

Abstract

Communication in the millimeter frequency band becomes a requirement with the launch of 5G hence the importance of the design of integrated antennas. The aperture-coupled microstrip patch antenna is a suitable choice for 5G application especially since it presents less losses compared to others microstrip antennas. This antenna must be adapted to the resonant frequency by operating in the frequency band allocated for automotive radar applications ranging from 77 GHz to 81 GHz. This article presents the prediction of the resonant frequency of an aperture coupled fed microstrip patch antenna in function of its physical input parameters by proposing two models. The first model is the 3D High-Frequency Electromagnetic Simulation Software model. The second model is the Artificial Neural Network learning model and the Levenberg-Marquardt which is adopted as a learning algorithm. The results revealed that the Artificial Neural Network learning model works at least ten thousand times faster than the High Frequency Electromagnetic Simulation Software model. It was further concluded that it can be used as a preliminary research tool to optimize design models for the aperture coupled microstrip patch antenna due to its speed of operation due to its overwhelming operating speed.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Mariem Abdi
AU  - Taoufik Aguili
PY  - 2022
DA  - 2022/02/02
TI  - Resonance Frequency Prediction of Aperture Coupled Microstrip Antenna Based on Artificial Neural Network
BT  - Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)
PB  - Atlantis Press
SP  - 176
EP  - 181
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.220201.031
DO  - 10.2991/aisr.k.220201.031
ID  - Abdi2022
ER  -