Analysis of Graphene-Silicon Based Two-Port THz Patch Antenna using Machine Learning Algorithms

The enhancement of MIMO antenna performance assumes a pivotal role in addressing the escalating demands of high-capacity wireless communication systems. These techniques, in tandem, contribute to improved data rates, expanded coverage, and a more dependable wireless connectivity. This paper proposes a hybrid twin port radiator with polarization and pattern diversity features in the THz frequency domain. The design is suitable for THz-based cellular machines due to its three main contributions: (i) a rounded wave is obtained by stimulating the silicon ceramic via fan blade formed slot; (ii) the |S11| of the designed aerial is effectively predicted by the Random Forest (RF) and XGBoost technique; (iii) the radiator is tunable due to the coating of graphene over silicon dielectric; the optimized result confirms that the planned aerial operates effectively between 2.46-3.16 THz with less than 3-dB axial ratio from 2.62-2.9 THz.