Antenna Design and Optimization Using Machine Learning: A Comprehensive Review

The upcoming era of Wireless communication systems is about to make significant use of deep learning and machine learning technology. Compared to traditional ground-based systems, the evolution of communication-based applications is expected to amplify coverage and spectrum efficiency. Machine learning and deep learning, with their capacity for optimizing solutions, find applications in various domains, including antenna design. Antennas, preferred for their robust computational processing, pristine data, and extensive data storage capabilities, are increasingly benefiting from these advanced technologies. This paper provides an extensive review of the recent developments and applications of machine learning (ML) in antenna design and optimization. Antennas are integral components of communication systems, and the integration of ML techniques has demonstrated significant potential in overcoming traditional design challenges. This review encompasses a rigorous analysis of ML methodologies applied to various aspects of antenna design, including parameter optimization, pattern synthesis, and performance enhancement. The paper aims to offer insights into the current state of the field, identify key challenges, and outline future directions for research and applications.