Predictive Forward Reverse Hybrid Modeling of Microstrip Antenna Using Machine Learning Based Regression and Classification Techniques

Abstract

This Ph.D. thesis presents the design and development of a new class of printed microstrip antennas optimized for space and satellite communication applications. With the rapid advancement of intelligent computational techniques, particularly machine learning (ML), conventional approaches to antenna design, analysis, and optimization have been significantly improved. In this framework, the thesis proposes an integrated ML-based modeling strategy that combines forward, inverse, and hybrid methodologies utilizing both regression and classification algorithms. The research focuses on accurately predicting key electrical parameters such as resonance, cut-off, and notched frequencies and estimating their corresponding geometrical configurations for various antenna architectures, including singleport, dual-port, and four-port models. These antenna designs are tailored to operate efficiently across the S-band to X-band frequency spectrum, meeting the high-performance requirements of contemporary satellite communication systems. The forward modelling phase, multiple supervised regression algorithms-such as Support Vector Regression (SVR), Gradient Boosted Decision Trees (GBDT), Ridge, Lasso, Elastic Net, and Deep neural networks (DNNs)-are implemented to predict frequency characteristics from antenna geometries. Conversely, in the reverse modelling approach, the algorithms predict optimal geometrical parameters from known electrical specifications. Hybrid models are further developed by integrating forward and reverse predictions to improve reliability and design efficiency. For antennas incorporating U-shaped slots and Electromagnetic Band Gap (EBG) structures, the ML models effectively learn the complex nonlinear relationships between inputs and outputs.