An Overview of the Design and Optimization of Antennas Employing Machine Learning Algorithms and Methodologies

Abstract

This paper offers a comprehensive exploration of the integration of machine learning (ML) techniques in antenna design and optimization, reflecting the burgeoning interest and potential impact of ML in emerging technologies. The first segment conducts a detailed literature survey, elucidating conventional computational electromagnetic and numerical methods preceding an in-depth discussion on the core facets of ML, encompassing diverse learning categories and frameworks. Additionally, it provides a mathematical exposition on regression models facilitated by ML algorithms, particularly emphasizing their application in antenna synthesis and analysis. The subsequent sections delve into specific research papers, meticulously examining various techniques and algorithms utilized for generating antenna parameters tailored to desired radiation characteristics and specifications. Notably, the categorization of investigated antennas based on type and configuration aids readers intending to engage with specific antenna types utilizing ML methodologies. Moreover, the abstract highlights the significance of ML in optimizing solutions for micro-strip patch antenna design, underscoring its utility for enhancing antenna performance and efficiency across a spectrum of applications, including millimeter wave, body-centric, terahertz, satellite, unmanned aerial vehicle, global positioning system, and textiles. Through meticulous analysis and discussion, this paper underscores the promising trajectory of ML and deep learning in revolutionizing antenna design processes, offering accelerated solutions and enhanced computational feasibility.