AI-Assisted Metasurface Antennas Design/Optimization and Performance Enhancement Techniques: A Comprehensive Survey

Metasurface optimization using machine learning has emerged as a rapidly developing field that harnesses the power of artificial intelligence to design and optimize metasurface antenna systems. Metasurface antenna design involves the process of manipulating electromagnetic waves by controlling their amplitude, phase, and polarization. The optimization aspect focuses on improving antenna characteristics to achieve desirable performance metrics such as gain, bandwidth, radiation pattern, and directivity. Metasurface antennas are two-dimensional arrays of subwavelength structures that manipulate electromagnetic waves with remarkable accuracy. The enhancement of metasurface antennas aims to improve their performance, functionality, and adaptability to satisfy contemporary communication and sensing requirements. This paper presents a review of current advances and developments in metasurface antenna design and optimization using AI techniques. The main objective of this survey is to examine various electromagnetic metasurfaces and metasurface antennas that utilize artificial intelligence approaches, such as machine learning and deep learning techniques, and to analyze their optimization capabilities, efficiency, and challenges. Currently, numerous metasurface antenna designs have been developed by researchers worldwide. However, a comprehensive study of different types of electromagnetic metasurfaces, metasurface antennas, and their applications combining AI and performance enhancement techniques remains lacking. This paper provides a comprehensive study that offers an in-depth exploration of various metasurface antenna designs, optimization methods, and their performance enhancement approaches.