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Haque MA, Saha D, Al-Bawri SS, Paul LC, Rahman MA, Alshanketi F, Alhazmi A, Rambe AH, Zakariya M, Ba Hashwan SS. Machine learning-based technique for resonance and directivity prediction of UMTS LTE band quasi Yagi antenna. Heliyon 2023; 9:e19548. [PMID: 37809766 PMCID: PMC10558792 DOI: 10.1016/j.heliyon.2023.e19548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 10/10/2023] Open
Abstract
In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent circuit, are discussed in this article as ways to assess an antenna's suitability for the intended applications. The CST simulation gives the suggested antenna a reflection coefficient of -38.40 dB at 2.1 GHz and a bandwidth of 357 MHz (1.95 GHz-2.31 GHz) at a -10 dB level. With a dimension of 0.535λ 0 × 0.714λ 0 , it is not only compact but also features a maximum gain of 6.9 dB, a maximum directivity of 7.67, VSWR of 1.001 at center frequency and a maximum efficiency of 89.9%. The antenna is made of a low-cost substrate, FR4. The RLC circuit, sometimes referred to as the lumped element model, exhibits characteristics that are sufficiently similar to those of the proposed Yagi antenna. We use yet another supervised regression machine learning (ML) technique to create an exact forecast of the antenna's frequency and directivity. The performance of machine learning (ML) models can be evaluated using a variety of metrics, including the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE). Out of the seven ML models, the linear regression (LR) model has the lowest error and maximum accuracy when predicting directivity, whereas the ridge regression (RR) model performs the best when predicting frequency. The proposed antenna is a strong candidate for the intended UMTS LTE applications, as shown by the modeling results from CST and ADS, as well as the measured and forecasted outcomes from machine learning techniques.
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Affiliation(s)
- Md. Ashraful Haque
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia
- Department of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1341, Bangladesh
| | - Dipon Saha
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia
| | - Samir Salem Al-Bawri
- Space Science Centre, Climate Change Institute, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Malaysia
- Department of Electronics & Communication Engineering, Faculty of Engineering & Petroleum, Hadhramout University, Al-Mukalla, 50512, Hadhramout, Yemen
| | - Liton Chandra Paul
- Department of Electrical, Electronic and Communication Engineering, Pabna University of Science and Technology, Pabna, Bangladesh
| | - Md Afzalur Rahman
- Space Science Centre, Climate Change Institute, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Malaysia
| | - Faisal Alshanketi
- Department of Computer Science, Jazan University, Jazan, Saudi Arabia
| | - Ali Alhazmi
- Department of Information Technology and Security, Jazan University, Jazan, 45142, Saudi Arabia
| | - Ali Hanafiah Rambe
- Department of Electrical Engineering, Universitas Sumatera Utara, Medan, Indonesia
| | - M.A. Zakariya
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia
| | - Saeed S. Ba Hashwan
- Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia
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Antenna Design for Microwave and Millimeter Wave Applications: Latest Advances and Prospects. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11125556] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Until recently, substantial effort has been devoted to new approaches and attempts to the design of antennas for microwave and millimeter-wave applications [...]
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