Abstract
This research introduces a novel design for a graphene-based multiple-input multiple-output (MIMO) antenna, specifically developed for sixth generation (6G) terahertz (THz) applications. The proposed antenna demonstrates multi-resonant behavior across three broad bandwidths: 2.479 THz (1.00-3.49 THz), 0.516 THz (3.64-4.156 THz), and 1.694 THz (4.50-6.20 THz). It achieves a peak gain of 13.41 dB, exceptional isolation of -34.2 dB, and a high efficiency of up to 90%. The design was developed using CST Studio Suite and validated through an RLC equivalent circuit model in ADS. This ensured accurate representation of its electromagnetic behavior. To improve the predictive capabilities of the antenna design process, five supervised regression-based machine learning (ML) models were employed. The models used were Extra Trees, Random Forest, Decision Tree, Ridge Regression, and Gaussian Process Regression. Among these, the Extra Trees Regression model delivered the highest prediction accuracy, achieving a mean absolute error (MAE) of 2.51%, a mean squared error (MSE) of 0.44%, and an R(2) score of 98.91%. The ML models were trained using a comprehensive dataset generated from parametric variations in patch, feed, substrate, and slot geometries. The proposed antenna leverages the advanced properties of graphene to deliver outstanding performance in gain, bandwidth, efficiency, and diversity metrics. It achieves a diversity gain of 9.9993 and an envelope correlation coefficient (ECC) as low as 0.00013. The integration of machine learning and RLC modeling reduces simulation time and improves design optimization. This creates a robust framework for future 6G wireless and biomedical THz applications.