Machine learning-based technique for gain and resonance prediction of mid band 5G Yagi antenna

Md Ashraful Haque; Md Afzalur Rahman; Samir Salem Al-Bawri; Zubaida Yusoff; Adiba Haque Sharker; Wazie M Abdulkawi; Dipon Saha; Liton Chandra Paul; M A Zakariya

doi:10.1038/s41598-023-39730-1

Machine learning-based technique for gain and resonance prediction of mid band 5G Yagi antenna

Sci Rep. 2023 Aug 3;13(1):12590. doi: 10.1038/s41598-023-39730-1.

Authors

Md Ashraful Haque^{1

2}, Md Afzalur Rahman³, Samir Salem Al-Bawri^{4

5}, Zubaida Yusoff⁶, Adiba Haque Sharker², Wazie M Abdulkawi⁷, Dipon Saha^{1

2}, Liton Chandra Paul⁸, M A Zakariya⁹

Affiliations

¹ Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak, Malaysia.
² Department of Electrical and Electronic Engineering, Daffodil International University, Birulia, Dhaka, Bangladesh.
³ Space Science Centre, Climate Change Institute, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Malaysia.
⁴ Space Science Centre, Climate Change Institute, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Malaysia. samir@ukm.edu.my.
⁵ Department of Electronics and Communication Engineering, Faculty of Engineering and Petroleum, Hadhramout University, 50512, Al-Mukalla, Hadhramout, Yemen. samir@ukm.edu.my.
⁶ Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia. zubaida@mmu.edu.my.
⁷ Department of Electrical Engineering, College of Engineering in Wadi Addawasir, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
⁸ Department of Electrical, Electronic and Communication Engineering, Pabna University of Science and Technology, Pabna, Bangladesh.
⁹ Smart Infrastructure Modelling and Monitoring (SIMM) Research Group Institute of Transportation and Infrastructure Universiti Teknologi PETRONAS, 32610, Bandar, Seri Iskandar, Perak, Malaysia.

Abstract

In this study, we present our findings from investigating the use of a machine learning (ML) technique to improve the performance of Quasi-Yagi-Uda antennas operating in the n78 band for 5G applications. This research study investigates several techniques, such as simulation, measurement, and an RLC equivalent circuit model, to evaluate the performance of an antenna. In this investigation, the CST modelling tools are used to develop a high-gain, low-return-loss Yagi-Uda antenna for the 5G communication system. When considering the antenna's operating frequency, its dimensions are [Formula: see text]. The antenna has an operating frequency of 3.5 GHz, a return loss of [Formula: see text] dB, a bandwidth of 520 MHz, a maximum gain of 6.57 dB, and an efficiency of almost 97%. The impedance analysis tools in CST Studio's simulation and circuit design tools in Agilent ADS software are used to derive the antenna's equivalent circuit (RLC). We use supervised regression ML method to create an accurate prediction of the frequency and gain of the antenna. Machine learning models can be evaluated using a variety of measures, including variance score, R square, mean square error, mean absolute error, root mean square error, and mean squared logarithmic error. Among the nine ML models, the prediction result of Linear Regression is superior to other ML models for resonant frequency prediction, and Gaussian Process Regression shows an extraordinary performance for gain prediction. R-square and var score represents the accuracy of the prediction, which is close to 99% for both frequency and gain prediction. Considering these factors, the antenna can be deemed an excellent choice for the n78 band of a 5G communication system.