Design and Development of Machine Learning Assisted Cylindrical Dielectric Resonator Antenna

Abstract

The design of a Dielectric Resonator Antenna (DRA) utilizing machine learning approaches is presented in this study. Antennas are an essential part of a wireless network. An antenna with a decent design will reduce system requirements and improve overall system performance. Full-wave electromagnetic simulation is exact and necessary in antenna design. Still, it takes more time to perform, resulting in enormous challenges in designing, optimizing, and performing sensitivity analysis. The data in this study is subjected to several Machine Learning (ML) algorithms for optimizing antenna efficiency and S-parameter value. To expedite antenna design, machine learning-assisted optimization (MLAO) has proven to be the most effective method. A wide range of regression techniques, including as Support Vector Machine (SVM), Decision Tree Regression (DTR), and Random Forest Regression (RFR), have been used in machine learning (ML) techniques to develop antenna models, enabling quick response prediction and optimum S11 value. Multiple MLAO algorithms have been implemented using these machine-learning techniques for various applications out of which Decision Tree Regression and Random Forest Regression outperforms among all the above-mentioned algorithms with 99.98% and 99.99% of accuracy. First, a broad overview of recent advancements in ML approaches for antenna modelling is presented.