Citation

Haque, Md. Ashraful and Nirob, Jamal Hossain and Nahin, Kamal Hossain and Md. Jizat, Noorlindawaty and Zakariya, M. A. and Ananta, Redwan A. and Abdulkawi, Wazie M. and Aljaloud, Khaled and Al-Bawri, Samir Salem (2025) Machine learning-based technique for gain prediction of mm-wave miniaturized 5G MIMO slotted antenna array with high isolation characteristics. Scientific Reports, 15 (1). ISSN 2045-2322

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Abstract

This study presents the design and analysis of a compact 28GHz MIMO antenna for 5G wireless networks, incorporating simulations, measurements, and machine learning (ML) techniques to optimize its performance. With dimensions of 3.19 λ₀×3.19 λ₀, the antenna offers a bandwidth of 5.1GHz, a peak gain of 9.43 dBi, high isolation of 31.37 dB, and an efficiency of 99.6%. Simulations conducted in CST Studio were validated through prototype measurements, showing strong agreement between the measured and simulated results. To further validate the design, an equivalent RLC circuit model was developed and analyzed using ADS, with the reflection coefficient results closely matching those from CST. Additionally, supervised ML techniques were employed to predict the antenna’s gain, evaluating nine models using metrics such as R-squared, variance score, mean absolute error, and root mean squared error. Among the models, Random Forest Regression achieved the highest accuracy, delivering approximately 99% reliability in gain prediction. This integration of machine learning with antenna design underscores its potential to optimize performance and enhance design efficiency. With its compact size, high isolation, and exceptional efficiency, the proposed antenna is a promising candidate for 28GHz 5G applications, offering innovative solutions for next-generation wireless communication.

Item Type:	Article
Uncontrolled Keywords:	28 GHz, 5G technology
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Feb 2025 03:02
Last Modified:	18 Feb 2025 03:02
URII:	http://shdl.mmu.edu.my/id/eprint/13469

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