High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction
Abstract This research outlines the results on implementing a Machine Learning (ML) approach to improve the throughput of Multiple-Input Multiple-Output (MIMO) based 5G millimeter wave applications. The research will cover frequencies between 28 and 38 GHz, significantly affecting high-band 5G appli...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-02646-z |
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| author | Md Ashraful Haque Redwan A. Ananta Md. Sharif Ahammed Jamal Hossain Nirob Narinderjit Singh Sawaran Singh Liton Chandra Paul Reem Ibrahim Alkanhel Ahmed A. Abd El-Latif May Almousa Abdelhamied A. Ateya |
| author_facet | Md Ashraful Haque Redwan A. Ananta Md. Sharif Ahammed Jamal Hossain Nirob Narinderjit Singh Sawaran Singh Liton Chandra Paul Reem Ibrahim Alkanhel Ahmed A. Abd El-Latif May Almousa Abdelhamied A. Ateya |
| author_sort | Md Ashraful Haque |
| collection | DOAJ |
| description | Abstract This research outlines the results on implementing a Machine Learning (ML) approach to improve the throughput of Multiple-Input Multiple-Output (MIMO) based 5G millimeter wave applications. The research will cover frequencies between 28 and 38 GHz, significantly affecting high-band 5G applications. We have chosen to employ a Rogers RT 5880 material with a low loss as the substrate layer to reduce the antenna size. In addition to being small, the recommended design has a maximum gain of 10.14 dB, better isolation than 29 dB, and wide bandwidth, ranging from 27.2 GHz to 32.2 GHz & 36.5 GHz to 40.7 GHz. Advanced design system (ADS) is used to make a circuit like the suggested microstrip patch antenna (MPA) to compare the reflection coefficient from CST. The approach of supervised regression machine learning is applied to accurately forecast the antenna’s gain. Among the five different regression machine learning models considered, it was discovered that the Random Forest Regression (RFR) model performed the best in accuracy and achieved the lowest error when predicting gain. This article explores many approaches, including simulation, integration of an RLC-equivalent circuit model, and multiple regression models, to evaluate the suitability of an antenna for its 5G applications. |
| format | Article |
| id | doaj-art-c923f92d12e145668da204848b31c69f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c923f92d12e145668da204848b31c69f2025-08-20T03:45:25ZengNature PortfolioScientific Reports2045-23222025-07-0115111710.1038/s41598-025-02646-zHigh-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain predictionMd Ashraful Haque0Redwan A. Ananta1Md. Sharif Ahammed2Jamal Hossain Nirob3Narinderjit Singh Sawaran Singh4Liton Chandra Paul5Reem Ibrahim Alkanhel6Ahmed A. Abd El-Latif7May Almousa8Abdelhamied A. Ateya9Department of Electrical and Electronic Engineering, Daffodil International UniversityDepartment of Electrical and Electronic Engineering, Daffodil International UniversityDepartment of Electrical and Electronic Engineering, Daffodil International UniversityDepartment of Electrical and Electronic Engineering, Daffodil International UniversityFaculty of Data Science and Information Technology, INTI International UniversityDepartment of Electrical, Electronic and Communication Engineering, Pabna University of Science and TechnologyDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan UniversityDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan UniversityAbstract This research outlines the results on implementing a Machine Learning (ML) approach to improve the throughput of Multiple-Input Multiple-Output (MIMO) based 5G millimeter wave applications. The research will cover frequencies between 28 and 38 GHz, significantly affecting high-band 5G applications. We have chosen to employ a Rogers RT 5880 material with a low loss as the substrate layer to reduce the antenna size. In addition to being small, the recommended design has a maximum gain of 10.14 dB, better isolation than 29 dB, and wide bandwidth, ranging from 27.2 GHz to 32.2 GHz & 36.5 GHz to 40.7 GHz. Advanced design system (ADS) is used to make a circuit like the suggested microstrip patch antenna (MPA) to compare the reflection coefficient from CST. The approach of supervised regression machine learning is applied to accurately forecast the antenna’s gain. Among the five different regression machine learning models considered, it was discovered that the Random Forest Regression (RFR) model performed the best in accuracy and achieved the lowest error when predicting gain. This article explores many approaches, including simulation, integration of an RLC-equivalent circuit model, and multiple regression models, to evaluate the suitability of an antenna for its 5G applications.https://doi.org/10.1038/s41598-025-02646-zDual-Band28/38 GHzArray antennaMIMOmm-WaveHigh-Gain |
| spellingShingle | Md Ashraful Haque Redwan A. Ananta Md. Sharif Ahammed Jamal Hossain Nirob Narinderjit Singh Sawaran Singh Liton Chandra Paul Reem Ibrahim Alkanhel Ahmed A. Abd El-Latif May Almousa Abdelhamied A. Ateya High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction Scientific Reports Dual-Band 28/38 GHz Array antenna MIMO mm-Wave High-Gain |
| title | High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction |
| title_full | High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction |
| title_fullStr | High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction |
| title_full_unstemmed | High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction |
| title_short | High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction |
| title_sort | high isolation dual band mimo antenna for next generation 5g wireless networks at 28 38 ghz with machine learning based gain prediction |
| topic | Dual-Band 28/38 GHz Array antenna MIMO mm-Wave High-Gain |
| url | https://doi.org/10.1038/s41598-025-02646-z |
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