High performance grid structured MIMO antenna with regression machine learning for high-speed sub THz and THz 6G IoT applications

This study introduces an innovative approach that leverages machine learning techniques to optimize antenna gain for next-generation wireless communication and Internet of Things (IoT) systems operating in the Terahertz (THz) frequency spectrum. Designed on a 160 × 160 μm² polyimide substrate, the antenna is analyzed using CST-2018 simulations and RLC circuit modeling. The proposed antenna demonstrates outstanding performance by achieving high peak gains of 11.91 dB and 12.21 dB across two operational bands. Exceptional isolation values of 31.43 dB and 36.1 dB are maintained in their respective bands, along with a high radiation efficiency of 92.42% and 86.93%. The design effectively covers two wide frequency ranges: 0.081–1.36 THz (1.2 THz bandwidth) and 1.81–3.43 THz (1.6 THz bandwidth), making it highly suitable for THz communication scenarios. To enhance the validation of the model, an analogous RLC equivalent model is constructed via ADS, yielding S11 that is nearly aligned with those obtained by CST-2018. Furthermore, supervised regression machine learning approaches are engaged to forecast the gain of MIMO antenna, assessing five distinct algorithms. Among these techniques, XGB Regression exhibited superior precision, attaining over 96% dependability gain in forecasting. The integration of regression models with MIMO design demonstrates potential for enhancing capacity and improving design efficiency. The suggested antenna, characterized by its compact dimensions, superior isolation, and remarkable efficiency, demonstrates significant potential for high-speed 6G applications, providing unique solutions for next-generation wireless communications.