Meta learner-based optimization for antenna efficiency prediction and high-performance THz MIMO antenna applications
A number of classical machine learning approaches have been used to predict antenna efficiency. However, machine learning needs to be enhanced to predict more accurately. The stacked generalization approach has been shown to be capable of learning from features and meta features. In this paper, we p...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302501953X |
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| author | Md. Ashraful Haque Md. Kawsar Ahmed Geamel Alyami Kamal Hossain Nahin Md Sharif Ahammed Akil Ahmad Taki Narinderjit Singh Sawaran Singh Md Afzalur Rahman Liton Chandra Paul Hussein Shaman |
| author_facet | Md. Ashraful Haque Md. Kawsar Ahmed Geamel Alyami Kamal Hossain Nahin Md Sharif Ahammed Akil Ahmad Taki Narinderjit Singh Sawaran Singh Md Afzalur Rahman Liton Chandra Paul Hussein Shaman |
| author_sort | Md. Ashraful Haque |
| collection | DOAJ |
| description | A number of classical machine learning approaches have been used to predict antenna efficiency. However, machine learning needs to be enhanced to predict more accurately. The stacked generalization approach has been shown to be capable of learning from features and meta features. In this paper, we propose a meta learner-based stacked generalization ensemble strategy that passes classical stacked ensemble output to an optimized multi-feature stacked ensemble. For the optimizer, a grid search strategy is employed. Applying an ANN model with a classical ML model as a base learner for predicting antenna efficiency leads to increased performance in terms of R2, EVS, MAE, and RMSE of 0.9998, 0.9998, 0.0001, and 0.0001, respectively, with MSE tending to zero. This improved accuracy significantly aids in designing our THz MIMO antenna, which resonates at frequencies of 4.99 THz and 8.831 THz with an extensive bandwidth of 5.941 THz. The MIMO configuration, utilizing graphene as the patch and copper as the ground, achieves an impressive isolation of -27.34 dB, a gain of 11.89 dB, and an efficiency of 88.90 %. The antenna exhibits exceptional diversity performance, with an Envelope Correlation Coefficient (ECC) as low as 0.007 and a Diversity Gain (DG) of 9.9654. Furthermore, the RLC circuit model precisely replicates the reflection coefficients of the MIMO antenna, affirming the accuracy and reliability of the predictive models. These results suggest that our optimized prediction model and high-performance MIMO antenna design are well-suited for advancing next-generation THz. |
| format | Article |
| id | doaj-art-aa6ef4a02fae4fd2810199afee4dff52 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-aa6ef4a02fae4fd2810199afee4dff522025-08-20T03:32:46ZengElsevierResults in Engineering2590-12302025-09-012710588210.1016/j.rineng.2025.105882Meta learner-based optimization for antenna efficiency prediction and high-performance THz MIMO antenna applicationsMd. Ashraful Haque0Md. Kawsar Ahmed1Geamel Alyami2Kamal Hossain Nahin3Md Sharif Ahammed4Akil Ahmad Taki5Narinderjit Singh Sawaran Singh6Md Afzalur Rahman7Liton Chandra Paul8Hussein Shaman9Department of Electrical & Electronic Engineering, Daffodil International University, BangladeshDepartment of Electrical & Electronic Engineering, Daffodil International University, BangladeshKing Abdulaziz’s city for science and technology (KACST), Saudi Arabia; Corresponding authors.Department of Electrical & Electronic Engineering, Daffodil International University, BangladeshDepartment of Electrical & Electronic Engineering, Daffodil International University, BangladeshDepartment of Electrical & Electronic Engineering, University of Asia Pacific, Bangladesh; Corresponding authors.Faculty of Data Science and Information Technology, INTI International University, Persiaran Perdana BBN, Putra Nilai, Nilai, Negeri Sembilan 71800, MalaysiaSpace Science Centre, Climate Change Institute, Universiti Kebangsaan Malaysia (UKM), Bangi, MalaysiaDepartment of Electrical, Electronic and Communication Engineering, Pabna University of Science and Technology, Pabna, BangladeshKing Abdulaziz’s city for science and technology (KACST), Saudi ArabiaA number of classical machine learning approaches have been used to predict antenna efficiency. However, machine learning needs to be enhanced to predict more accurately. The stacked generalization approach has been shown to be capable of learning from features and meta features. In this paper, we propose a meta learner-based stacked generalization ensemble strategy that passes classical stacked ensemble output to an optimized multi-feature stacked ensemble. For the optimizer, a grid search strategy is employed. Applying an ANN model with a classical ML model as a base learner for predicting antenna efficiency leads to increased performance in terms of R2, EVS, MAE, and RMSE of 0.9998, 0.9998, 0.0001, and 0.0001, respectively, with MSE tending to zero. This improved accuracy significantly aids in designing our THz MIMO antenna, which resonates at frequencies of 4.99 THz and 8.831 THz with an extensive bandwidth of 5.941 THz. The MIMO configuration, utilizing graphene as the patch and copper as the ground, achieves an impressive isolation of -27.34 dB, a gain of 11.89 dB, and an efficiency of 88.90 %. The antenna exhibits exceptional diversity performance, with an Envelope Correlation Coefficient (ECC) as low as 0.007 and a Diversity Gain (DG) of 9.9654. Furthermore, the RLC circuit model precisely replicates the reflection coefficients of the MIMO antenna, affirming the accuracy and reliability of the predictive models. These results suggest that our optimized prediction model and high-performance MIMO antenna design are well-suited for advancing next-generation THz.http://www.sciencedirect.com/science/article/pii/S259012302501953XTHZ antennaMimo antennaMachine learning6G CommunicationRLC |
| spellingShingle | Md. Ashraful Haque Md. Kawsar Ahmed Geamel Alyami Kamal Hossain Nahin Md Sharif Ahammed Akil Ahmad Taki Narinderjit Singh Sawaran Singh Md Afzalur Rahman Liton Chandra Paul Hussein Shaman Meta learner-based optimization for antenna efficiency prediction and high-performance THz MIMO antenna applications Results in Engineering THZ antenna Mimo antenna Machine learning 6G Communication RLC |
| title | Meta learner-based optimization for antenna efficiency prediction and high-performance THz MIMO antenna applications |
| title_full | Meta learner-based optimization for antenna efficiency prediction and high-performance THz MIMO antenna applications |
| title_fullStr | Meta learner-based optimization for antenna efficiency prediction and high-performance THz MIMO antenna applications |
| title_full_unstemmed | Meta learner-based optimization for antenna efficiency prediction and high-performance THz MIMO antenna applications |
| title_short | Meta learner-based optimization for antenna efficiency prediction and high-performance THz MIMO antenna applications |
| title_sort | meta learner based optimization for antenna efficiency prediction and high performance thz mimo antenna applications |
| topic | THZ antenna Mimo antenna Machine learning 6G Communication RLC |
| url | http://www.sciencedirect.com/science/article/pii/S259012302501953X |
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