Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approaches
In recent years, improvements in wireless communication have led to the development of microstrip or patch antennas. The article discusses using simulation, measurement, an RLC equivalent circuit model, and machine learning to assess antenna performance. The antenna's dimensions are 1.01 λ0×0.6...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823007500 |
| _version_ | 1797665382813663232 |
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| author | Md. Ashraful Haque M.A. Zakariya Samir Salem Al-Bawri Zubaida Yusoff Mirajul Islam Dipon Saha Wazie M. Abdulkawi Md Afzalur Rahman Liton Chandra Paul |
| author_facet | Md. Ashraful Haque M.A. Zakariya Samir Salem Al-Bawri Zubaida Yusoff Mirajul Islam Dipon Saha Wazie M. Abdulkawi Md Afzalur Rahman Liton Chandra Paul |
| author_sort | Md. Ashraful Haque |
| collection | DOAJ |
| description | In recent years, improvements in wireless communication have led to the development of microstrip or patch antennas. The article discusses using simulation, measurement, an RLC equivalent circuit model, and machine learning to assess antenna performance. The antenna's dimensions are 1.01 λ0×0.612λ0 with respect to the lowest operating frequency, the maximum achieved gain is 6.76 dB, the maximum directivity is 8.21 dBi, and the maximum efficiency is 83.05%. The prototype's measured return loss is compared to CST and ADS simulations. The prediction of gain and directivity of the antenna is determined using a different supervised regression machine learning (ML) method. The performance of ML models is measured by the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE), etc. With errors of less than unity and an accuracy of roughly 98%, Ridge regression gain prediction outperforms the other seven ML models. Gaussian process regression is the best method for predicting directivity. Finally, modeling results from CST and ADS, as well as measured and anticipated results from machine learning, reveal that the suggested antenna is a good candidate for LTE. |
| first_indexed | 2024-03-11T19:43:03Z |
| format | Article |
| id | doaj.art-022ec5ef0b5440aea157e238e931c2d3 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-11T19:43:03Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-022ec5ef0b5440aea157e238e931c2d32023-10-06T04:44:01ZengElsevierAlexandria Engineering Journal1110-01682023-10-0180383396Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approachesMd. Ashraful Haque0M.A. Zakariya1Samir Salem Al-Bawri2Zubaida Yusoff3Mirajul Islam4Dipon Saha5Wazie M. Abdulkawi6Md Afzalur Rahman7Liton Chandra Paul8Department 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; Corresponding author at: Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia.Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia; Smart Infrastructure Modelling and Monitoring (SIMM) Research Group Institute of Transportation and Infrastructure Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610 Perak, MalaysiaSpace 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; Corresponding author at: Space Science Centre, Climate Change Institute, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Malaysia.Faculty of Engineering, Multimedia University, Cyberjaya 63100, Selangor, Malaysia; Corresponding author at: Faculty of Engineering, Multimedia University(MMU), 63100, Selangor, Malaysia.Dept. of Computer Science and Engineering, Daffodil International University, Dhaka 1341, BangladeshDepartment 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, BangladeshDepartment of Electrical Engineering, College of Engineering in Wadi Addawasir, Prince Sattam bin Abdulaziz University, Al-Kharj 11991, Saudi ArabiaDepartment of Electrical and Electronic Engineering, Daffodil International University, Dhaka 1341, Bangladesh; Space Science Centre, Climate Change Institute, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, MalaysiaDepartment of Electrical, Electronic and Communication Engineering, Pabna University of Science and Technology, Pabna, BangladeshIn recent years, improvements in wireless communication have led to the development of microstrip or patch antennas. The article discusses using simulation, measurement, an RLC equivalent circuit model, and machine learning to assess antenna performance. The antenna's dimensions are 1.01 λ0×0.612λ0 with respect to the lowest operating frequency, the maximum achieved gain is 6.76 dB, the maximum directivity is 8.21 dBi, and the maximum efficiency is 83.05%. The prototype's measured return loss is compared to CST and ADS simulations. The prediction of gain and directivity of the antenna is determined using a different supervised regression machine learning (ML) method. The performance of ML models is measured by the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE), etc. With errors of less than unity and an accuracy of roughly 98%, Ridge regression gain prediction outperforms the other seven ML models. Gaussian process regression is the best method for predicting directivity. Finally, modeling results from CST and ADS, as well as measured and anticipated results from machine learning, reveal that the suggested antenna is a good candidate for LTE.http://www.sciencedirect.com/science/article/pii/S1110016823007500Yagi antennaLong-term evolutionCSTADSMachine learning |
| spellingShingle | Md. Ashraful Haque M.A. Zakariya Samir Salem Al-Bawri Zubaida Yusoff Mirajul Islam Dipon Saha Wazie M. Abdulkawi Md Afzalur Rahman Liton Chandra Paul Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approaches Alexandria Engineering Journal Yagi antenna Long-term evolution CST ADS Machine learning |
| title | Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approaches |
| title_full | Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approaches |
| title_fullStr | Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approaches |
| title_full_unstemmed | Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approaches |
| title_short | Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approaches |
| title_sort | quasi yagi antenna design for lte applications and prediction of gain and directivity using machine learning approaches |
| topic | Yagi antenna Long-term evolution CST ADS Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823007500 |
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