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

Aug 5, 2026

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This article explores machine learning techniques, focusing on 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 WLAN operation between 5.6 GHz and 5.85 GHz. The antenna has dimensions 30×30×1.6 mm³, centered at 5.725 GHz, and uses a coaxial probe feed with a circular slot to enhance bandwidth. Results show ML models outperform traditional simulation tools, enabling efficient exploration of design configurations and accurate electrical/physical characterization, with GPR achieving the lowest MSE and highest R².

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Abstract

Introduction

Motivation for ML in antenna optimization

Related work and prior ML approaches

[https://doi.org/10.2298/FUEE2404687V](https://doi.org/10.2298/FUEE2404687V) Original scientific paper MACHINE LEARNING ASSISTED S11 PREDICTION FOR A SLOTTED SQUARE PATCH ANTENNA IN 5.8 GHZ WLAN BAND􀀍 Doshant Verma1, Pinku Ranjan2, Alka Verma3, Pankaj Kumar Goswami4, Neeraj Kaushik5 1,3,4,5Department of Electronics & Communication Engineering, Teerthanker Mahaveer University, Moradabad, India 2Department of Electrical and Electronics Engineering, ABV-Indian Institute of Information Technology and Management, Gwalior (MP), India ORCID iDs: Doshant Verma [https://orcid.org/0009-0000-3723-3412](https://orcid.org/0009-0000-3723-3412) Pinku Ranjan [https://orcid.org/0000-0002-1422-5943](https://orcid.org/0000-0002-1422-5943) Alka Verma [https://orcid.org/0000-0002-0726-2017](https://orcid.org/0000-0002-0726-2017) Pankaj Kumar Goswami [https://orcid.org/0000-0002-1066-747X](https://orcid.org/0000-0002-1066-747X) Neeraj Kaushik [https://orcid.org/0000-0002-0990-4183](https://orcid.org/0000-0002-0990-4183) 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. Key words: Machine Learning, mean square error, S11, bandwidth 1. INTRODUCTION Machine learning (ML) has achieved huge attention for its capability to automate tasks and provide deep insights across diverse scientific and engineering disciplines. While still developing, ML has notably impacted several industries, including antenna design and optimization. ML has brought forward innovative methods that enhance efficiency, adaptability, and performance in antenna systems. Traditionally, antenna design was heavily Received May 02, 2024; revised July 04, 2024, August 16, 2024 and August 31, 2024; accepted September 09, 2024 Corresponding author: Pinku Ranjan Department of Electrical and Electronics Engineering, ABV- Indian Institute of Information Technology and Management, Gwalior (M.P.), India [E-mail: pinkuranjan@iiitm.ac.in](E-mail: pinkuranjan@iiitm.ac.in) reliant on complex mathematical models and precise parameter adjustments to achieve optimal performance. This traditional approach involved several steps, including defining the antenna shape and tuning parameters using simulation tools, which, although accurate, required considerable manual effort and iterations. On the other hand, ML techniques streamline the prediction and optimization of antenna parameters, making the process much faster and simpler compared to traditional simulation tools. These methods are remarkably favorable for handling complex antenna designs and high-dimensional parameter spaces. The effectiveness of ML methods [1-2] is affected by factors such as the complexity of the design, the optimization algorithm used, and the level of automation in the ML models. By integrating simulation tools with ML methods, a more comprehensive and efficient approach to antenna optimization can be achieved, improving the overall design evaluation process. This combined approach leverages the strengths of both methodologies, resulting in a more rapid and effective optimization workflow [3-4] . In [5],

FAQs

Which machine learning methods are used to predict the antenna S11 parameter?

Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Gaussian Process Regression (GPR) are used to predict the S11 parameter of the slotted square patch antenna.

What frequency range and antenna structure are targeted in the study?

The study targets WLAN operation between 5.6 GHz and 5.85 GHz using a 30×30×1.6 mm³ slotted square patch antenna with a circular slot and a coaxial probe feed.

How does Gaussian Process Regression perform compared with ANN and SVM?

GPR consistently shows lower Mean Squared Error (MSE) and higher R-squared (R²) values than ANN and SVM, indicating better predictive accuracy for antenna performance metrics.

Aug 5, 2026

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