Machine Learning-Based Design and Analysis of Antenna Systems: A Comprehensive Investigation

A major breakthrough in wireless communication has taken place in recent years. Today, individuals depend on cloud computing, the Internet of Things, and applications based on big data analytics. Increased data speeds, quicker send/receive intervals, greater coverage and increased throughputs are essential for such applications. Modern Antennas are among the vital elements of contemporary technology.  ML is clearly showing a promising-outlook in optimizing antenna design by accurately predicting antenna performance as well as speeding up the optimization process. This discussion introduces a groundbreaking approach utilizing machine learning to design and optimize multi-band patch antennas primarily targeting advanced IoT applications in beyond-5G and 6G networks. Acknowledging the constraints of conventional antenna design techniques, there are several machine learning algorithms including Decision Tree, Random Forest, Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Extra Trees, CatBoost, and Gradient Boosting are discussed in terms of significance, merits, and demerits.