Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches
Abstract This paper introduces the design and exploration of a compact, high-performance multiple-input multiple-output (MIMO) antenna for 6G applications operating in the terahertz (THz) frequency range. Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-88174-2 |
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| author | Kamal Hossain Nahin Jamal Hossain Nirob Akil Ahmad Taki Md Ashraful Haque Narinderjit Sawaran SinghSingh Liton Chandra Paul Reem Ibrahim Alkanhel Hanaa A. Abdallah Abdelhamied A. Ateya Ahmed A. Abd El-Latif |
| author_facet | Kamal Hossain Nahin Jamal Hossain Nirob Akil Ahmad Taki Md Ashraful Haque Narinderjit Sawaran SinghSingh Liton Chandra Paul Reem Ibrahim Alkanhel Hanaa A. Abdallah Abdelhamied A. Ateya Ahmed A. Abd El-Latif |
| author_sort | Kamal Hossain Nahin |
| collection | DOAJ |
| description | Abstract This paper introduces the design and exploration of a compact, high-performance multiple-input multiple-output (MIMO) antenna for 6G applications operating in the terahertz (THz) frequency range. Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. Specifically, a neural network is applied as a base learner for predicting antenna parameters, resulting in increased predictive performance, achieving R², EVS, MSE, RMSE, and MAE values of 0.96, 0.998, 0.00842, 0.00453, and 0.00999, respectively. Utilizing regression-based machine learning, antenna parameters are optimized to attain dual-band resonance with bandwidths of 3.34 THz and 1 THz across two bands, ensuring robust data throughput and communication stability. The antenna, designed with dimensions of 70 × 280 μm², demonstrates a maximum gain of 15.82 dB, excellent isolation exceeding − 32.9 dB, and remarkable efficiency of 99.8%, underscoring its suitability for high-density, high-speed 6G environments. The design methodology integrates CST simulations and an RLC equivalent circuit model, substantiated by ADS simulations, with comparable reflection coefficients validating the accuracy of the models. With its compact footprint, broad bandwidth, and optimized isolation and efficiency, the proposed MIMO antenna is positioned as an ideal candidate for future 6G communication applications. |
| format | Article |
| id | doaj-art-011517444d7e4294a83eda44c8a64d41 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-011517444d7e4294a83eda44c8a64d412025-02-09T12:29:13ZengNature PortfolioScientific Reports2045-23222025-02-0115112910.1038/s41598-025-88174-2Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approachesKamal Hossain Nahin0Jamal Hossain Nirob1Akil Ahmad Taki2Md Ashraful Haque3Narinderjit Sawaran SinghSingh4Liton Chandra Paul5Reem Ibrahim Alkanhel6Hanaa A. Abdallah7Abdelhamied A. Ateya8Ahmed A. Abd El-Latif9Department 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 University, Negeri SembilanDepartment 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 UniversityDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman UniversityEIAS Data Science Lab, College of Computer and Information Sciences, and Center of Excellence in Quantum and Intelligent Computing, Prince Sultan UniversityEIAS Data Science Lab, College of Computer and Information Sciences, and Center of Excellence in Quantum and Intelligent Computing, Prince Sultan UniversityAbstract This paper introduces the design and exploration of a compact, high-performance multiple-input multiple-output (MIMO) antenna for 6G applications operating in the terahertz (THz) frequency range. Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. Specifically, a neural network is applied as a base learner for predicting antenna parameters, resulting in increased predictive performance, achieving R², EVS, MSE, RMSE, and MAE values of 0.96, 0.998, 0.00842, 0.00453, and 0.00999, respectively. Utilizing regression-based machine learning, antenna parameters are optimized to attain dual-band resonance with bandwidths of 3.34 THz and 1 THz across two bands, ensuring robust data throughput and communication stability. The antenna, designed with dimensions of 70 × 280 μm², demonstrates a maximum gain of 15.82 dB, excellent isolation exceeding − 32.9 dB, and remarkable efficiency of 99.8%, underscoring its suitability for high-density, high-speed 6G environments. The design methodology integrates CST simulations and an RLC equivalent circuit model, substantiated by ADS simulations, with comparable reflection coefficients validating the accuracy of the models. With its compact footprint, broad bandwidth, and optimized isolation and efficiency, the proposed MIMO antenna is positioned as an ideal candidate for future 6G communication applications.https://doi.org/10.1038/s41598-025-88174-2 |
| spellingShingle | Kamal Hossain Nahin Jamal Hossain Nirob Akil Ahmad Taki Md Ashraful Haque Narinderjit Sawaran SinghSingh Liton Chandra Paul Reem Ibrahim Alkanhel Hanaa A. Abdallah Abdelhamied A. Ateya Ahmed A. Abd El-Latif Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches Scientific Reports |
| title | Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches |
| title_full | Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches |
| title_fullStr | Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches |
| title_full_unstemmed | Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches |
| title_short | Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches |
| title_sort | performance prediction and optimization of a high efficiency tessellated diamond fractal mimo antenna for terahertz 6g communication using machine learning approaches |
| url | https://doi.org/10.1038/s41598-025-88174-2 |
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