Machine Learning Enabled Compact Frequency-Tunable Triple-Band Hexagonal-Shaped Graphene Antenna for THz Communication

Abstract

In this article, a compact triple-band frequency-tunable (FT) hexagonal-shaped graphene antenna through a machine learning (ML) approach for terahertz (THz) application is presented. The proposed THz antenna is designed on a polyamide (${\in }_r=3.5$) substrate with a thickness of 10 μm, and graphene is used as an antenna radiator. The size of the substrate is 38 × 46 μm². The FT is achieved by changing the chemical potential of graphene material. The performance of the proposed THz antenna has been investigated, and the impacts of several conducting materials like gold, aluminum, copper, and graphene and dielectric materials like Rogers RT/duroid 5880, polyamide, quartz, and SiO₂ are explored. The proposed THz antenna provides three operating bands. The frequency of operation in Band-1 is 2.51–5.05 THz, Band-2 is 5.99–7.43 THz, and Band-3 is 7.94–9.63 THz. The bandwidth in Band-1, Band-2, and Band-3 are 2.54, 1.44, and 1.69 THz, respectively. The % of impedance bandwidth in Band-1, Band-2, and Band-3 are 67.19%, 24.02%, and 21.28% respectively. The proposed antenna has a maximum peak gain of 5 dBi. The proposed antenna is optimized through various ML algorithms like random forest (RF), extreme gradient boosting (XGB), K-nearest neighbor (KNN), decision tree (DT), and artificial neural network (ANN). The RF algorithm gives more than 99% accuracy compared to other ML algorithms and accurately predicts the S₁₁ of the proposed antenna. The proposed THz antenna would be suitable for applications related to imaging, medical, sensing, and ultra-speed short-distance communication applications in the THz region.