Optimization of Antenna Performance for IoT Applications Using Machine Learning Techniques

The integration of machine learning (ML) into antenna optimization has revolutionized the design and enhancement of antenna systems. This paper provides an in-depth review of the latest advancements in antenna design optimization through ML techniques. The goal of this survey is to highlight various ML optimization methods and their effectiveness, along with the challenges they pose in terms of efficiency. A comprehensive literature review is presented, detailing the optimization algorithms and procedures employed to achieve specific antenna performance metrics, including gain, bandwidth, radiation pattern, and impedance matching. The paper begins with an overview of machine learning and its algorithms, followed by a concise explanation of the antenna optimization process. Subsequently, it introduces different printed antenna designs optimized using ML algorithms. The techniques discussed in this survey are poised to significantly impact the future development of antennas for diverse wireless applications.