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Design of frequency reconfigurable planar antenna using artificial neural network

Published online by Cambridge University Press:  14 October 2021

Navneet Kaur Open the ORCID record for Navneet Kaur [Opens in a new window] ,
Jagtar Singh Sivia  and
Rajni
Navneet Kaur*
Affiliation:
Department of Electronics and Communication Engineering, Punjabi University, Patiala, Punjab, India
Jagtar Singh Sivia
Affiliation:
YCoE, Punjabi University Guru Kashi Campus, Talwandi Sabo, Bathinda, Punjab, India
Rajni
Affiliation:
Shaheed Bhagat Singh State Technical Campus, Ferozepur, Punjab, India
*
Author for correspondence: Navneet Kaur, E-mail: navsandhu31696@gmail.com
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Abstract

In this paper, the design of frequency reconfigurable planar antenna by incorporation of metasurface superstrate (FRPA-MSS) is presented using an artificial neural network. The dual-layer radiating structure is created on a 1.524 mm thick Rogers RO4350B substrate board (εr = 3.48, tan δ = 0.0037). The candidate antenna is designed and analyzed using a high-frequency structure simulator (HFSS) tool. The transfer matrix method is employed for the successful retrieval of electromagnetic properties of the metamaterial. Frequency reconfiguration is achieved by placing the metasurface superstrate onto the rectangular patch antenna. A simplified ANN approach has been employed for the design of metasurface incorporated proposed antenna. Presented prototypes are characterized through experimental measurements. It is found from the practical observations that the proposed antenna effectively reconfigures the tuning range from 5.03 to 6.13 GHz. Moreover, the presented antenna operates efficiently with agreeable gain, good impedance matching, and stable pattern characteristics across the entire operational bandwidth. The experimental results obtained validate the simulated performance.

Keywords

Artificial electromagnetic materialsartificial neural networkmetamaterialmetasurface superstratereconfigurable antennaS-parameterswireless communications
Type
Antenna Design, Modelling and Measurements
Information
International Journal of Microwave and Wireless Technologies , Volume 14 , Issue 9 , November 2022 , pp. 1107 - 1118
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press in association with the European Microwave Association

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