Machine learning-based novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applications
Abstract This study discusses the results of using a regression machine learning technique to improve the performance of 6G applications that use multiple-input multiple-output (MIMO) antennas operating at the terahertz (THz) frequency band. This research evaluates an antenna’s performance using var...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-79332-z |
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| author | Md Ashraful Haque Kamal Hossain Nahin Jamal Hossain Nirob Redwan A. Ananta Narinderjit Singh Sawaran Singh Liton Chandra Paul Abeer D. Algarni Mohammed ElAffendi Abdelhamied A. Ateya |
| author_facet | Md Ashraful Haque Kamal Hossain Nahin Jamal Hossain Nirob Redwan A. Ananta Narinderjit Singh Sawaran Singh Liton Chandra Paul Abeer D. Algarni Mohammed ElAffendi Abdelhamied A. Ateya |
| author_sort | Md Ashraful Haque |
| collection | DOAJ |
| description | Abstract This study discusses the results of using a regression machine learning technique to improve the performance of 6G applications that use multiple-input multiple-output (MIMO) antennas operating at the terahertz (THz) frequency band. This research evaluates an antenna’s performance using various methodologies, such as simulation and RLC equivalent circuit models. The suggested design has a broad bandwidth of 2.5 THz and spans from 6.2 to 8.7 GHz, a maximum gain of 14.59 dB, and small dimensions (100 × 300) µm2. It also has outstanding isolation exceeding − 31 dB with 96% efficiency. The ADS allowed us to confirm the accuracy of the CST results by creating a simulated version of the same RLC circuit. Reflection coefficients obtained from the CST and ADS simulators are similar. The supervised regression ML approach is employed accurately to predict the antenna’s potential gain. Several metrics, such as the variance score, R square, mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE), can evaluate machine learning (ML) models. Out of the six machine learning models analyzed, the Extra Tree Regression model demonstrates the lowest error and achieves the highest level of accuracy in predicting gain. |
| format | Article |
| id | doaj-art-8b5f21220f044310997984b678d1dffe |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8b5f21220f044310997984b678d1dffe2025-01-05T12:28:02ZengNature PortfolioScientific Reports2045-23222024-12-0114112710.1038/s41598-024-79332-zMachine learning-based novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applicationsMd Ashraful Haque0Kamal Hossain Nahin1Jamal Hossain Nirob2Redwan A. Ananta3Narinderjit Singh Sawaran Singh4Liton Chandra Paul5Abeer D. Algarni6Mohammed ElAffendi7Abdelhamied A. Ateya8Department 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 UniversityEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan UniversityAbstract This study discusses the results of using a regression machine learning technique to improve the performance of 6G applications that use multiple-input multiple-output (MIMO) antennas operating at the terahertz (THz) frequency band. This research evaluates an antenna’s performance using various methodologies, such as simulation and RLC equivalent circuit models. The suggested design has a broad bandwidth of 2.5 THz and spans from 6.2 to 8.7 GHz, a maximum gain of 14.59 dB, and small dimensions (100 × 300) µm2. It also has outstanding isolation exceeding − 31 dB with 96% efficiency. The ADS allowed us to confirm the accuracy of the CST results by creating a simulated version of the same RLC circuit. Reflection coefficients obtained from the CST and ADS simulators are similar. The supervised regression ML approach is employed accurately to predict the antenna’s potential gain. Several metrics, such as the variance score, R square, mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE), can evaluate machine learning (ML) models. Out of the six machine learning models analyzed, the Extra Tree Regression model demonstrates the lowest error and achieves the highest level of accuracy in predicting gain.https://doi.org/10.1038/s41598-024-79332-z |
| spellingShingle | Md Ashraful Haque Kamal Hossain Nahin Jamal Hossain Nirob Redwan A. Ananta Narinderjit Singh Sawaran Singh Liton Chandra Paul Abeer D. Algarni Mohammed ElAffendi Abdelhamied A. Ateya Machine learning-based novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applications Scientific Reports |
| title | Machine learning-based novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applications |
| title_full | Machine learning-based novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applications |
| title_fullStr | Machine learning-based novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applications |
| title_full_unstemmed | Machine learning-based novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applications |
| title_short | Machine learning-based novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applications |
| title_sort | machine learning based novel shaped thz mimo antenna with a slotted ground plane for future 6g applications |
| url | https://doi.org/10.1038/s41598-024-79332-z |
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