Citation

Haque, Ashraful and Hossain Nahin, Kamal and Tiang, Jun Jiat and Hasan, Mehidy and Singh Sawaran Singh, Narinderjit and Kader Jilani, Abdul (2025) High-Gain Dielectric Resonator Antenna for 6G Sub-THz Wireless Networks and Terahertz Sensing. Journal of Communications, 20 (5). pp. 632-639. ISSN 17962021

Text JCM-V20N5-632.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

This article presents the design and evaluation of a Rectangular Dielectric Resonator Antenna (RDRA) for sub-THz applications, specifically at a frequency of 0.49 THz. The antenna exhibits promising performance with a return loss of −35.45 dB, a bandwidth of 351 GHz, and an efficiency of approximately 85%. With a compact size of 59 μm × 59 μm, the RDRA offers a high-performance solution for next-generation wireless communication systems. The antenna’s performance was assessed through software simulations, an RLC equivalent circuit model, and Machine Learning (ML) techniques. The resonance frequencies predicted by the Resistor, Inductor, Capacitor (RLC) model, simulated using Advanced Design System (ADS) Agilent software, align closely with those from other modeling tools, validating the accuracy of the design. Furthermore, five regression-based ML models were developed to predict the dielectric antenna’s gain, with the XGB regression model demonstrating the best prediction accuracy. The study also explores the efficacy of the ML models using several evaluation metrics, including variance score, R-squared, Mean Square Error (MSE), and Root Mean Square Error (RMSE). The results highlight the potential of the RDRA for sub-THz applications, such as ultra-fast wireless communication and high-resolution imaging, with ML enhancing the design optimization process.

Item Type:	Article
Uncontrolled Keywords:	6G, advanced design system (ADS), dielectric resonator antenna
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:	Nurin Syazwani Azmi
Date Deposited:	03 Dec 2025 07:57
Last Modified:	03 Dec 2025 08:24
URII:	http://shdl.mmu.edu.my/id/eprint/14944

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