Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches

This paper introduces the design and exploration of a compact, high-performance multiple-input multiple-output (MIMO) antenna for 6G applications operating in the terahertz (THz) frequency range. Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. Specifically, a neural network is applied as a base learner for predicting antenna parameters, resulting in increased predictive performance, achieving R², EVS, MSE, RMSE, and MAE values of 0.96, 0.998, 0.00842, 0.00453, and 0.00999, respectively. Utilizing regression-based machine learning, antenna parameters are optimized to attain dual-band resonance with bandwidths of 3.34 THz and 1 THz across two bands, ensuring robust data throughput and communication stability. The antenna, designed with dimensions of 70 × 280 μm², demonstrates a maximum gain of 15.82 dB, excellent isolation exceeding - 32.9 dB, and remarkable efficiency of 99.8%, underscoring its suitability for high-density, high-speed 6G environments. The design methodology integrates CST simulations and an RLC equivalent circuit model, substantiated by ADS simulations, with comparable reflection coefficients validating the accuracy of the models. With its compact footprint, broad bandwidth, and optimized isolation and efficiency, the proposed MIMO antenna is positioned as an ideal candidate for future 6G communication applications.