MACHINE LEARNING ASSISTED S11 PREDICTION FOR A SLOTTED SQUARE PATCH ANTENNA IN 5.8 GHZ WLAN BAND

Doshant Verma, Pinku Ranjan, Alka Verma, Pankaj Kumar Goswami, Neeraj Kaushik

DOI Number
https://doi.org/10.2298/FUEE2404687V
First page
687
Last page
701

Abstract


This article explores machine learning techniques, specifically Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Gaussian Process Regression (GPR), to predict the S11 parameter of a slotted square patch antenna optimized for Wireless Local Area Network (WLAN) operation between 5.6 GHz and 5.85 GHz. The antenna, measuring 30x30x1.6 mm³ and centered at 5.725 GHz, features a coaxial probe feed design with a circular slot within the square patch to enhance bandwidth. These ML methods demonstrate superior efficiency compared to traditional simulation tools, enabling robust exploration of design configurations and accurate prediction of the antenna's electrical and physical characteristics. Notably, Gaussian Process Regression (GPR) consistently reveal lower Mean Squared Error (MSE) and higher R-squared (R²) values than ANN and SVM, suggesting superior accuracy in modeling the antenna's performance metrics.

Keywords

Machine Learning, mean square error, S11, bandwidth

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References


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