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

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 4, 2025

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

Language: Английский

Comparative Performance Analysis of Two Novel Design MIMO Antennas for 5G and Wi-Fi 6 Applications

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103808 - 103808

Published: Dec. 1, 2024

Language: Английский

Design of Sub-6 GHz BPF Using Chained Even and Odd Mode Admittance Polynomials For 5G C-Band Applications

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103614 - 103614

Published: Dec. 1, 2024

Language: Английский