Ramasamy R, V. Rajavel, Rachit Jain
This study investigates the design and performance of millimeter-wave (mm-Wave) Multiple-Input Multiple-Output (MIMO) antennas for fifth-generation (5G) applications, with a particular focus on the consequences of incorporating a ring resonator within the antenna system. This study compares two design variations, one with a ring resonator, and one without to assess their impact on enhancing the antenna's performance characteristics. The research employs five machine learning algorithms, namely, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), XG-Boost, and Gradient Boosting Regressor (GBR), to estimate return loss. Among these, the Random Forest algorithm demonstrates superior performance in terms of accuracy, Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and R-squared metrics. The proposed MIMO antenna system shows better performance in Envelope Correlation Coefficient (ECC), Diversity Gain (DG), Channel Capacity Loss (CCL) and Total Active Reflection Coefficient (TARC). The results indicate that including a ring resonator in the antenna design significantly improves the antenna's performance, and machine learning algorithms, particularly Random Forest, can effectively predict and optimize critical parameters for antenna design in 5G applications.