Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna
Abstract Machine Learning (ML) has significantly transformed antenna design by enabling efficient optimization of geometrical parameters, modeling complex electromagnetic behavior, and accelerating performance prediction with reduced computational cost. This study presents an ML-based approach to ac...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Journal of King Saud University: Engineering Sciences |
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| Online Access: | https://doi.org/10.1007/s44444-025-00010-0 |
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| author | Md Mahabub Alam Nurhafizah Abu Talip Yusof Ahmad Afif Mohd Faudzi Md Raihanul Islam Tomal Md Ershadul Haque Md. Suaibur Rahman |
| author_facet | Md Mahabub Alam Nurhafizah Abu Talip Yusof Ahmad Afif Mohd Faudzi Md Raihanul Islam Tomal Md Ershadul Haque Md. Suaibur Rahman |
| author_sort | Md Mahabub Alam |
| collection | DOAJ |
| description | Abstract Machine Learning (ML) has significantly transformed antenna design by enabling efficient optimization of geometrical parameters, modeling complex electromagnetic behavior, and accelerating performance prediction with reduced computational cost. This study presents an ML-based approach to accurately predict the resonance frequencies and bandwidth of a novel triple-band circular Substrate Integrated Waveguide (SIW) antenna intended for K- and Ka-band satellite communication. The antenna features four symmetrically etched ring slots on the radiating patch and circularly arranged vias within a Rogers RT/Duroid 5880 substrate (20 × 15 × 1.6 mm3). The interaction between the TE₁₁ cavity mode and the ring slots facilitates controlled electromagnetic field leakage, enhancing radiation performance. A predictive ML framework was developed using six regression algorithms trained on significant geometrical parameters, such as ring slot radius, via diameter, and feedline width. Among them, the Extra Trees Regression model achieved over 98% accuracy, with errors below 0.1% for both resonance frequency and bandwidth predictions. The approach was validated through Computer Simulation Technology (CST) simulations and ML-based predictions, both demonstrating strong agreement. Although experimental fabrication was not included in this phase, the model offers a reliable foundation for future physical validation and prototyping. The results confirm that the proposed antenna structure, combined with the predictive power of ML, presents a promising solution for compact, high-performance antennas in satellite communication systems. |
| format | Article |
| id | doaj-art-a3440cd371d84fb784d88c060a684dcf |
| institution | Kabale University |
| issn | 1018-3639 2213-1558 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Engineering Sciences |
| spelling | doaj-art-a3440cd371d84fb784d88c060a684dcf2025-08-20T04:01:40ZengSpringerJournal of King Saud University: Engineering Sciences1018-36392213-15582025-07-0137411910.1007/s44444-025-00010-0Machine learning-based approach for bandwidth and frequency prediction of circular SIW antennaMd Mahabub Alam0Nurhafizah Abu Talip Yusof1Ahmad Afif Mohd Faudzi2Md Raihanul Islam Tomal3Md Ershadul Haque4Md. Suaibur Rahman5Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan AbdullahFaculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan AbdullahFaculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan AbdullahFaculty of Computing, Universiti Malaysia Pahang Al-Sultan AbdullahSchool of Computing Mathematics and Engineering, Charles Sturt UniversityDepartment of Electrical and Electronics Engineering, Islamic University of TechnologyAbstract Machine Learning (ML) has significantly transformed antenna design by enabling efficient optimization of geometrical parameters, modeling complex electromagnetic behavior, and accelerating performance prediction with reduced computational cost. This study presents an ML-based approach to accurately predict the resonance frequencies and bandwidth of a novel triple-band circular Substrate Integrated Waveguide (SIW) antenna intended for K- and Ka-band satellite communication. The antenna features four symmetrically etched ring slots on the radiating patch and circularly arranged vias within a Rogers RT/Duroid 5880 substrate (20 × 15 × 1.6 mm3). The interaction between the TE₁₁ cavity mode and the ring slots facilitates controlled electromagnetic field leakage, enhancing radiation performance. A predictive ML framework was developed using six regression algorithms trained on significant geometrical parameters, such as ring slot radius, via diameter, and feedline width. Among them, the Extra Trees Regression model achieved over 98% accuracy, with errors below 0.1% for both resonance frequency and bandwidth predictions. The approach was validated through Computer Simulation Technology (CST) simulations and ML-based predictions, both demonstrating strong agreement. Although experimental fabrication was not included in this phase, the model offers a reliable foundation for future physical validation and prototyping. The results confirm that the proposed antenna structure, combined with the predictive power of ML, presents a promising solution for compact, high-performance antennas in satellite communication systems.https://doi.org/10.1007/s44444-025-00010-0SIWResonance frequencyBandwidthMachine learningK and Ka-bandSatellite communication |
| spellingShingle | Md Mahabub Alam Nurhafizah Abu Talip Yusof Ahmad Afif Mohd Faudzi Md Raihanul Islam Tomal Md Ershadul Haque Md. Suaibur Rahman Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna Journal of King Saud University: Engineering Sciences SIW Resonance frequency Bandwidth Machine learning K and Ka-band Satellite communication |
| title | Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna |
| title_full | Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna |
| title_fullStr | Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna |
| title_full_unstemmed | Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna |
| title_short | Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna |
| title_sort | machine learning based approach for bandwidth and frequency prediction of circular siw antenna |
| topic | SIW Resonance frequency Bandwidth Machine learning K and Ka-band Satellite communication |
| url | https://doi.org/10.1007/s44444-025-00010-0 |
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