Machine learning based compact MIMO antenna array for 38 GHz millimeter wave application with robust isolation and high efficiency performance
The performance of wireless 5 G communication networks can be enhanced by combining multiple-input multiple-output (MIMO) antennas with machine learning (ML). The suggested antenna, which is constructed on a Rogers 5880 substrate, is well-suited for usage in the high bands of 5 G applications due to...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025000945 |
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| author | Md. Ashraful Haque Md. Sharif Ahammed Soeung Socheatra Redwan A. Ananta Md. Jamal Hossain Nirob Narinderjit Singh Sawaran Singh Noorlindawaty Md. Jizat Saeed Alsowail Samir Salem Al-Bawri |
| author_facet | Md. Ashraful Haque Md. Sharif Ahammed Soeung Socheatra Redwan A. Ananta Md. Jamal Hossain Nirob Narinderjit Singh Sawaran Singh Noorlindawaty Md. Jizat Saeed Alsowail Samir Salem Al-Bawri |
| author_sort | Md. Ashraful Haque |
| collection | DOAJ |
| description | The performance of wireless 5 G communication networks can be enhanced by combining multiple-input multiple-output (MIMO) antennas with machine learning (ML). The suggested antenna, which is constructed on a Rogers 5880 substrate, is well-suited for usage in the high bands of 5 G applications due to its 27 dB isolation and bandwidth of 35.181–39.689 (4.508) GHz within a -10 dB range. Besides being compact (measuring 37 mm × 24 mm), it boasts an impressive maximum gain of 8.09 dB and an efficiency level of 98.2 %. The methods explored in this research are the RLC equivalent circuit model and simulations with validated measurements. An advanced design system (ADS) is utilized to compare the return loss because of CST to create a model like the suggested MPA. The next step is extensive data sampling using CST MWS simulation, followed by applying supervised regression ML techniques. Lasso regression yields excellent results in terms of accuracy and achieves the lowest degree of error when testing the bandwidth prediction. Considering everything, the antenna stands out as a top choice for the 5 G communication system's high band. Designing a small MIMO antenna for 38 GHz mm-wave 5 G applications presents challenges because it requires balancing high performance while minimizing mutual coupling between closely spaced elements and dealing with high-frequency complexities. |
| format | Article |
| id | doaj-art-32f490e834c94be1a20b81e101c2e686 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-32f490e834c94be1a20b81e101c2e6862025-01-19T06:26:34ZengElsevierResults in Engineering2590-12302025-03-0125104006Machine learning based compact MIMO antenna array for 38 GHz millimeter wave application with robust isolation and high efficiency performanceMd. Ashraful Haque0Md. Sharif Ahammed1Soeung Socheatra2Redwan A. Ananta3Md. Jamal Hossain Nirob4Narinderjit Singh Sawaran Singh5Noorlindawaty Md. Jizat6Saeed Alsowail7Samir Salem Al-Bawri8Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia; Department of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1207 BangladeshDepartment of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1207 BangladeshDepartment of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Perak, MalaysiaDepartment of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1207 BangladeshDepartment of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1207 BangladeshFaculty of Data Science and Information Technology, INTI International University, Nilai, MalaysiaFaculty of Engineering, Multimedia University, Cyberjaya, 62300, Malaysia; Corresponding authors.Information Technology Department, Tatweer Educational Technologies, 12264, Riyadh, Saudi ArabiaSpace Science Centre, Climate Change Institute, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Malaysia; Department of Computer Science & Information Technology, Gulf Colleges, Hafar Al-Batin, Saudi Arabia; Corresponding authors.The performance of wireless 5 G communication networks can be enhanced by combining multiple-input multiple-output (MIMO) antennas with machine learning (ML). The suggested antenna, which is constructed on a Rogers 5880 substrate, is well-suited for usage in the high bands of 5 G applications due to its 27 dB isolation and bandwidth of 35.181–39.689 (4.508) GHz within a -10 dB range. Besides being compact (measuring 37 mm × 24 mm), it boasts an impressive maximum gain of 8.09 dB and an efficiency level of 98.2 %. The methods explored in this research are the RLC equivalent circuit model and simulations with validated measurements. An advanced design system (ADS) is utilized to compare the return loss because of CST to create a model like the suggested MPA. The next step is extensive data sampling using CST MWS simulation, followed by applying supervised regression ML techniques. Lasso regression yields excellent results in terms of accuracy and achieves the lowest degree of error when testing the bandwidth prediction. Considering everything, the antenna stands out as a top choice for the 5 G communication system's high band. Designing a small MIMO antenna for 38 GHz mm-wave 5 G applications presents challenges because it requires balancing high performance while minimizing mutual coupling between closely spaced elements and dealing with high-frequency complexities.http://www.sciencedirect.com/science/article/pii/S2590123025000945MIMOMm-wave antenna5GMobile communication38 GHzRLC |
| spellingShingle | Md. Ashraful Haque Md. Sharif Ahammed Soeung Socheatra Redwan A. Ananta Md. Jamal Hossain Nirob Narinderjit Singh Sawaran Singh Noorlindawaty Md. Jizat Saeed Alsowail Samir Salem Al-Bawri Machine learning based compact MIMO antenna array for 38 GHz millimeter wave application with robust isolation and high efficiency performance Results in Engineering MIMO Mm-wave antenna 5G Mobile communication 38 GHz RLC |
| title | Machine learning based compact MIMO antenna array for 38 GHz millimeter wave application with robust isolation and high efficiency performance |
| title_full | Machine learning based compact MIMO antenna array for 38 GHz millimeter wave application with robust isolation and high efficiency performance |
| title_fullStr | Machine learning based compact MIMO antenna array for 38 GHz millimeter wave application with robust isolation and high efficiency performance |
| title_full_unstemmed | Machine learning based compact MIMO antenna array for 38 GHz millimeter wave application with robust isolation and high efficiency performance |
| title_short | Machine learning based compact MIMO antenna array for 38 GHz millimeter wave application with robust isolation and high efficiency performance |
| title_sort | machine learning based compact mimo antenna array for 38 ghz millimeter wave application with robust isolation and high efficiency performance |
| topic | MIMO Mm-wave antenna 5G Mobile communication 38 GHz RLC |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025000945 |
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