Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications

The rapid evolution of Internet of Things (IoT) applications demands advancements in wireless communication technologies to handle increasing data rates and connectivity requirements. This article presents our novel research on utilizing machine learning techniques to enhance the efficiency of MIMO antennas for Wireless Communication and IoT applications in the Terahertz (THz) frequency band. Our research assesses antenna performance using various methodologies, including simulation and RLC equivalent circuit models. The proposed design operates at 6.51 THz, 7.48 THz, and 8.46 THz, with bandwidths of 0.7 THz, 0.69 THz, and 0.89 THz, respectively. It features a maximum gain of 13.53 dBi and compact dimensions of 160 × 75 μm2. Additionally, it demonstrates excellent isolation, exceeding -32 dB, -44 dB, and -45 dB across these bands, with over 96.5% efficiency in all operating bands. By designing a similar RLC circuit in ADS and simulating it, we validated the results obtained from CST. Both CST and ADS simulators produced comparable reflection coefficients. Furthermore, several machine learning algorithms were applied to test the design. Various metrics, including variance score, R-squared, mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE), were used to evaluate the machine learning models. Among the five models analyzed, the Gradient Boosting Regression model exhibited the lowest error rates (4.94% MAE, 6.60% MSE, and 4.13% RMSE) and achieved the highest accuracy, exceeding 98% in predicting isolation. Considering all these factors, it is evident that this antenna is an excellent choice for the THz band in 6G wireless communication.