Citation

Haque, Md. Ashraful and Ahmed, Nizamuddin and Saifullah, Gazi Mohammad and Hasan, Mehidy and Tiang, Jun Jiat and Singh, Narinderjit Singh Sawaran and Algarni, Abeer D. and Ateya, Abdelhamied A. (2026) Machine learning-assisted validation of a high-isolation THz MIMO antenna for 6G communication and IoT application. Scientific Reports, 16 (1). ISSN 2045-2322

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Abstract

The terahertz (THz) frequency spectrum, which enables ultra-high data rates, low-latency communication (URLLC), and vast connectivity, is poised to be a key component in advancing wireless technology for 6G and beyond. But creating THz antennas that can overcome obstacles such as high propagation loss and complex construction is still quite difficult. A compact 1×2 MIMO patch antenna with dimensions of 130.7 μm×288 μm is shown in this study. It is optimized for dual-frequency bands at 8.20–9.63 THz and 9.95–10.86 THz, making it ideal for secure communication and high-resolution photography. The suggested antenna outperforms current designs, achieving an astounding 14.07 dB of gain, 87.7% efficiency. Moreover, the MIMO antenna’s capability is confirmed by an isolation of −44.64 dB, an ECC of 1.07×10–10, a DG of 9.99 dB, TARC and CCL values less than−10 dB and 0.4 bps/Hz, respectively. Machine learning (ML) techniques, specifically regression models such as Extra Trees, were used to optimize design parameters to improve performance. The result was remarkable prediction accuracy, with an R2 value of 98.13%, a mean absolute error (MAE) of 1.31%, and a root mean square error (RMSE) of 1.76%.

Item Type:	Article
Uncontrolled Keywords:	THz MIMO antenna, Regression machine learning
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Sep 2026 04:35
Last Modified:	04 Sep 2026 04:35
URII:	http://shdl.mmu.edu.my/id/eprint/16717

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