Design and Optimization of Dual Port Dielectric Resonator Based Frequency Tunable MIMO Antenna with Machine Learning Approach for 5G New Radio Application

In this article, a dual port Multiple Input Multiple Output (MIMO) cylindrical Dielectric Resonator (DR)-based frequency tunable antenna with a machine learning (ML) approach for a 5G New Radio (NR) application is presented. According to the author's best knowledge, it is the first time-frequency tunable MIMO hybrid DR with ML is reported. A dual port MIMO DRA is placed in the orthogonal configuration with the connected ground to obtain higher isolation (|S-12| < - 19 dB) in the entire frequency range. The proposed dual port antenna provides a total spectrum (TS) and tuning range (TR) of 98.99% and 80.93%, respectively. The different MIMO parameters, Envelope Correlation Coefficient (ECC), Total Active Reflection Coefficient (TARC), and Diversity Gain (DG) are investigated and found within the acceptable limits. The optimization of the proposed dual port tunable antenna is done through the various ML algorithms, including Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB). The KNN ML algorithm provides more than 98% accuracy for predicting the S-parameters in all configurations. Hence, the proposed antenna is suitable for 5G NR applications.