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Exploring Innovative Methods for Dielectric Resonator Antenna Design with HFSS and Machine Learning Integration
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 24 |
| Year of Publication: 2024 |
| Authors: Manupati Varshini Suraj, Ramineni Padmasree, Malothu Ankitha |
10.5120/ijca2024923696
|
Manupati Varshini Suraj, Ramineni Padmasree, Malothu Ankitha . Exploring Innovative Methods for Dielectric Resonator Antenna Design with HFSS and Machine Learning Integration. International Journal of Computer Applications. 186, 24 ( Jun 2024), 17-22. DOI=10.5120/ijca2024923696
Abstract
The Dielectric Resonator Antenna (DRA) stands out as a distinctive antenna type, diverging from traditional metallic components by employing a dielectric resonator, which leverages the benefits of its high permittivity dielectric material. Functioning at precise frequencies, DRAs play diverse roles in microwave and millimeter-wave communication systems. Crafting and refining such antennas involves careful selection of dielectric materials, shaping the resonator, and fine-tuning for specific frequency characteristics. Central to the design and analysis of DRAs is the High-Frequency Structure Simulator (HFSS), which plays an essential role. Notably, the integration of machine learning-assisted optimization (MLAO) significantly streamlines this process. This study concentrates on designing cylindrical DRAs operating at 4 GHz using HFSS. Meticulously prepared datasets encompass output parameters like reflection coefficient, achieved by varying the height of CDRA from 5mm to 15mm. By employing various machine learning algorithms such as Support Vector Machine (SVM), Random Forest, Decision Tree Regression, and Gaussian Process Regression to enhance performance, the study conducts a comprehensive analysis to identify the most effective algorithm for accurately predicting antenna characteristics. Particularly noteworthy is the consistent 100% accuracy achieved by Decision Tree Regression, irrespective of variations in the antenna height. The study underscores the collaborative potential between electromagnetic simulation tools and advanced machine learning techniques in the realm of antenna engineering.
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