Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications
Abstract The rapid evolution of Internet of Things (IoT) applications demands advancements in wireless communication technologies to handle increasing data rates and connectivity requirements. This article presents our novel research on utilizing machine learning techniques to enhance the efficiency...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-89962-6 |
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| author | Md Ashraful Haque Kamal Hossain Nahin Jamal Hossain Nirob Md. Kawsar Ahmed Narinderjit Singh Sawaran Singh Liton Chandra Paul Abeer D. Algarni Mohammed ElAffendi Ahmed A. Abd El-Latif Abdelhamied A. Ateya |
| author_facet | Md Ashraful Haque Kamal Hossain Nahin Jamal Hossain Nirob Md. Kawsar Ahmed Narinderjit Singh Sawaran Singh Liton Chandra Paul Abeer D. Algarni Mohammed ElAffendi Ahmed A. Abd El-Latif Abdelhamied A. Ateya |
| author_sort | Md Ashraful Haque |
| collection | DOAJ |
| description | Abstract The rapid evolution of Internet of Things (IoT) applications demands advancements in wireless communication technologies to handle increasing data rates and connectivity requirements. This article presents our novel research on utilizing machine learning techniques to enhance the efficiency of MIMO antennas for Wireless Communication and IoT applications in the Terahertz (THz) frequency band. Our research assesses antenna performance using various methodologies, including simulation and RLC equivalent circuit models. The proposed design operates at 6.51 THz, 7.48 THz, and 8.46 THz, with bandwidths of 0.7 THz, 0.69 THz, and 0.89 THz, respectively. It features a maximum gain of 13.53 dBi and compact dimensions of 160 × 75 μm2. Additionally, it demonstrates excellent isolation, exceeding −32 dB, −44 dB, and −45 dB across these bands, with over 96.5% efficiency in all operating bands. By designing a similar RLC circuit in ADS and simulating it, we validated the results obtained from CST. Both CST and ADS simulators produced comparable reflection coefficients. Furthermore, several machine learning algorithms were applied to test the design. Various metrics, including variance score, R-squared, mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE), were used to evaluate the machine learning models. Among the five models analyzed, the Gradient Boosting Regression model exhibited the lowest error rates (4.94% MAE, 6.60% MSE, and 4.13% RMSE) and achieved the highest accuracy, exceeding 98% in predicting isolation. Considering all these factors, it is evident that this antenna is an excellent choice for the THz band in 6G wireless communication. |
| format | Article |
| id | doaj-art-d2aad8723ba240ce81b19da9a2170cb4 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d2aad8723ba240ce81b19da9a2170cb42025-08-20T02:59:20ZengNature PortfolioScientific Reports2045-23222025-03-0115112310.1038/s41598-025-89962-6Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applicationsMd Ashraful Haque0Kamal Hossain Nahin1Jamal Hossain Nirob2Md. Kawsar Ahmed3Narinderjit Singh Sawaran Singh4Liton Chandra Paul5Abeer D. Algarni6Mohammed ElAffendi7Ahmed A. Abd El-Latif8Abdelhamied 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 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 The rapid evolution of Internet of Things (IoT) applications demands advancements in wireless communication technologies to handle increasing data rates and connectivity requirements. This article presents our novel research on utilizing machine learning techniques to enhance the efficiency of MIMO antennas for Wireless Communication and IoT applications in the Terahertz (THz) frequency band. Our research assesses antenna performance using various methodologies, including simulation and RLC equivalent circuit models. The proposed design operates at 6.51 THz, 7.48 THz, and 8.46 THz, with bandwidths of 0.7 THz, 0.69 THz, and 0.89 THz, respectively. It features a maximum gain of 13.53 dBi and compact dimensions of 160 × 75 μm2. Additionally, it demonstrates excellent isolation, exceeding −32 dB, −44 dB, and −45 dB across these bands, with over 96.5% efficiency in all operating bands. By designing a similar RLC circuit in ADS and simulating it, we validated the results obtained from CST. Both CST and ADS simulators produced comparable reflection coefficients. Furthermore, several machine learning algorithms were applied to test the design. Various metrics, including variance score, R-squared, mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE), were used to evaluate the machine learning models. Among the five models analyzed, the Gradient Boosting Regression model exhibited the lowest error rates (4.94% MAE, 6.60% MSE, and 4.13% RMSE) and achieved the highest accuracy, exceeding 98% in predicting isolation. Considering all these factors, it is evident that this antenna is an excellent choice for the THz band in 6G wireless communication.https://doi.org/10.1038/s41598-025-89962-6THz antennaMIMO antenna6G communicationRLCGrapheneHigh efficiency |
| spellingShingle | Md Ashraful Haque Kamal Hossain Nahin Jamal Hossain Nirob Md. Kawsar Ahmed Narinderjit Singh Sawaran Singh Liton Chandra Paul Abeer D. Algarni Mohammed ElAffendi Ahmed A. Abd El-Latif Abdelhamied A. Ateya Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications Scientific Reports THz antenna MIMO antenna 6G communication RLC Graphene High efficiency |
| title | Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications |
| title_full | Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications |
| title_fullStr | Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications |
| title_full_unstemmed | Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications |
| title_short | Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications |
| title_sort | multiband thz mimo antenna with regression machine learning techniques for isolation prediction in iot applications |
| topic | THz antenna MIMO antenna 6G communication RLC Graphene High efficiency |
| url | https://doi.org/10.1038/s41598-025-89962-6 |
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