Metasurface frequency reconfigurable antenna optimizes using neural network algorithm for wireless applications

Vishnu D; T. A. Shahul Hameed; Sheeba O; Chetan Barde; Prakash Ranjan

doi:10.1515/freq-2023-0417

Article

Metasurface frequency reconfigurable antenna optimizes using neural network algorithm for wireless applications

Vishnu D , T. A. Shahul Hameed , Sheeba O , Chetan Barde and Prakash Ranjan

Published/Copyright: June 19, 2024

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From the journal Frequenz Volume 78 Issue 9-10

Abstract

As the demand increases in the field of wireless communication system, the interest of researchers increases to develop and analyzes the antenna for these applications. This article present metasurface (MS) frequency reconfigurable antenna (FRA) which is optimized using neural network (NN) approach. The designed structure is a multilayer consists of three-layer patch antenna placed under the MS structure. The resonating patch is a rectangular shaped and substrate is a circular shaped show as to reduce the geometry of the antenna. The proposed structure is fabricated on FR-4 substrate of thickness 1.6 mm. The MS structure consists of split ring rectangular (SRR) strips made up of copper. The antenna reconfigured the operating frequency from 4.85 to 7 GHz having overall bandwidth of 2.15 GHz with wide range of tuning. The central frequency of rectangular patch antenna is 6.15 GHz. The MS is analyzed by using effective parameters i.e., effective permittivity (ε_r) & effective permeability (μ_r) and it is observed that the MS is behaving as a metamaterial in the desired range of frequency. The reconfigured operating frequency (ROF) is found at the anticlockwise rotation angles of 0°, 30°, 600 and 90°. The realized gain and radiation efficiency are calculated at each ROF. The validation of proposed MS based FRA is carried out first by simulating using Ansys HFSS and then measured inside the anechoic chamber. The proposed antenna is optimizes using NN model which shows minimum error during analysis and synthesis process.

Keywords: metasurface; frequency reconfigurable antenna; neural network; effective permittivity; effective permeability; reconfigured operating frequency

Corresponding author: Chetan Barde, Indian Institute of Information Technology Bhagalpur, Bihar, India, E-mail: 2017rsec004@nitjsr.ac.in

Acknowledgments

I would like to thank IIIT Bhagalpur for providing lab for testing facilities.

Research ethics: All the authors adhered to the accepted ethical standards of a genuine research study.
Author contributions: All authors ensures that the publisher has the author’s permission to publish research findings.
Competing interests: There is no competing of interest.
Research funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Data availability: There is no data associated with this manuscript.

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Received: 2023-11-23

Accepted: 2024-05-22

Published Online: 2024-06-19

Published in Print: 2024-10-28

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Articles in the same Issue

https://doi.org/10.1515/freq-2023-0417

Keywords for this article

metasurface; frequency reconfigurable antenna; neural network; effective permittivity; effective permeability; reconfigured operating frequency