A new study focuses on the design and performance prediction of graphene-based MIMO (Multiple-Input Multiple-Output) antennas, intended for sixth-generation (6G) communication systems operating in the terahertz (THz) range. The research addresses the need for efficient and compact communication components for future high-speed networks, exploring how graphene can offer significant advantages at these frequencies.

The work includes the development of an RLC (resistance-inductance-capacitance) equivalent circuit model to characterize the behavior of these graphene antennas. Furthermore, machine learning (ML) and deep learning (DL) have been employed to predict antenna performance, enabling faster design optimization and evaluation of their feasibility in complex operational scenarios. This computational approach is crucial for exploring the vast design space of THz antennas.