Design of machine learning optimized THz MIMO antenna for next-generation wireless communication systems

To meet the growing demand for high data rates in densely populated mobile networks, the development of compact and efficient antenna designs is necessary for the implementation of 5G and 6G technologies. The use of MIMO antenna technology shows promises in reducing network congestion. The present study investigates the benefits associated with the utilization of graphene in terahertz (THz) antenna applications. This investigation culminates in the development of a distinctive multiband Double Graphene Stripline Slot Patch – MIMO (DGSSP-MIMO) antenna, which operates at three frequencies: 4.71 THz, 8.55 THz, and 9 THz. When graphene was added, the reflection response (S11) decreased to −37.2783 dB, indicating that the signal was well contained within the antenna, though the gain was 8.46 dB lower than that of antennas without graphene (12.94 dB). We obtained very good results (R 2 = 0.976822) when tuning the parameters using machine learning optimization techniques. This demonstrates that graphene could be used to create high-performance terahertz antennas. The DGSSP-MIMO antenna, with its multiband response, has a wide range of applications in wireless communication and 6G technology.