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.

ANN-based performance estimation of a slotted inverted F-shaped tri-band antenna for satellite/mm-wave 5G application

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.

Dual-band MIMO antenna for wideband THz communication in future 6G applications

Graphene-based THz antenna with a wide bandwidth for future 6G short-range communication

This study introduces a compact, high-performance multiple-input multiple-output (MIMO) antenna engineered for 6G terahertz (THz) communication systems. The antenna is implemented on a polyimide substrate (dielectric constant εr = 3.5, loss tangent tanδ = 0.0027) with dimensions of 405 × 163.75 μm², providing a miniaturized footprint suitable for integrated wireless devices. The antenna exhibits multi-resonance operation at 3.752, 4.204, 4.652, 5.104, 5.548, 6.000, and 6.460 THz, providing corresponding bandwidths of 0.3348, 0.2634, 0.2389, 0.2289, 0.2158, 0.2063, and 0.2096 THz, ensuring wideband coverage suitable for high-data-rate applications. The antenna achieves a peak gain of 13.353 dB, outstanding isolation of − 34.044 dB, and high efficiency of 96.048%, highlighting its suitability for high-data-rate and low-interference 6G communications. Strong diversity performance is demonstrated through an ultra-low envelope correlation coefficient (ECC) of 0.00017856 and a near-ideal diversity gain (DG) of 9.99911, confirming the effectiveness of the proposed design for interference mitigation and channel reliability. CST Microwave Studio (MWS) simulations were employed to generate datasets for supervised regression machine learning to predict antenna gain. Random Forest Regression delivered superior predictive accuracy with MSE = 0.76%, MAE = 5.43%, RMSE = 8.72%, R² = 93.93%, and variance score = 95.12%, closely matching the simulated results. The integration of high-performance multi-band antenna design with regression-based machine learning demonstrates a reliable framework for rapid performance evaluation. The combination of compact geometry, wide multi-band operation, high gain, strong isolation, and machine learning-based predictive modeling positions the proposed antenna as a promising solution for high-data-rate and interference-resilient 6G THz communication networks.