Optimal design of transmitarray antennas via low-cost surrogate modelling
Abstract Over the recent years, reflectarrays and transmitarrays have been drawing a considerable attention due to their attractive features, including a possibility of realizing high gain and pencil-like radiation patterns without the employment of complex feeding networks. Among the two, transmita...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42134-w |
| _version_ | 1797453249096187904 |
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| author | Mehmet A. Belen Alper Caliskan Slawomir Koziel Anna Pietrenko-Dabrowska Peyman Mahouti |
| author_facet | Mehmet A. Belen Alper Caliskan Slawomir Koziel Anna Pietrenko-Dabrowska Peyman Mahouti |
| author_sort | Mehmet A. Belen |
| collection | DOAJ |
| description | Abstract Over the recent years, reflectarrays and transmitarrays have been drawing a considerable attention due to their attractive features, including a possibility of realizing high gain and pencil-like radiation patterns without the employment of complex feeding networks. Among the two, transmitarrays seem to be superior over reflectarrays in terms of achieving high radiation efficiency without the feed blockage. Notwithstanding, the design process of transmitarrays is more intricate due to the necessity of manipulating both the transmission phase and magnitude of its unit elements. For reliability, the design process has to be conducted at the level of full-wave electromagnetic models, which makes direct optimization prohibitive. The most widely used workaround is to employ surrogate modeling techniques to construct fast representations of the unit elements, yet the initial model setup cost is typically high and includes acquisition of thousands of training data points. In this paper, we propose a novel approach to cost-efficient design of transmitarrays. It is based on artificial-intelligence-enabled data-driven surrogates, which can be constructed using only a few hundreds of training data samples, while exhibiting the predictive power sufficient for reliable design. Our methodology is demonstrated by re-using the presented surrogate for the design of high-performance transmitarrays operating at various frequency ranges of 8–14 GHz, 22–28 GHz, and 28–36 GHz. |
| first_indexed | 2024-03-09T15:20:04Z |
| format | Article |
| id | doaj.art-f94dfea53f4048b0bdf7307d1d32e689 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:20:04Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f94dfea53f4048b0bdf7307d1d32e6892023-11-26T12:53:26ZengNature PortfolioScientific Reports2045-23222023-09-0113111810.1038/s41598-023-42134-wOptimal design of transmitarray antennas via low-cost surrogate modellingMehmet A. Belen0Alper Caliskan1Slawomir Koziel2Anna Pietrenko-Dabrowska3Peyman Mahouti4Department of Electric and Electronic Engineering, Iskenderun Technical University Department of Electronic and Communication Engineering, Yildiz Technical UniversityEngineering Optimization & Modeling Center, Department of Technology, Reykjavik UniversityFaculty of Electronics, Telecommunications and Informatics, Gdansk University of TechnologyDepartment of Electronic and Communication Engineering, Yildiz Technical UniversityAbstract Over the recent years, reflectarrays and transmitarrays have been drawing a considerable attention due to their attractive features, including a possibility of realizing high gain and pencil-like radiation patterns without the employment of complex feeding networks. Among the two, transmitarrays seem to be superior over reflectarrays in terms of achieving high radiation efficiency without the feed blockage. Notwithstanding, the design process of transmitarrays is more intricate due to the necessity of manipulating both the transmission phase and magnitude of its unit elements. For reliability, the design process has to be conducted at the level of full-wave electromagnetic models, which makes direct optimization prohibitive. The most widely used workaround is to employ surrogate modeling techniques to construct fast representations of the unit elements, yet the initial model setup cost is typically high and includes acquisition of thousands of training data points. In this paper, we propose a novel approach to cost-efficient design of transmitarrays. It is based on artificial-intelligence-enabled data-driven surrogates, which can be constructed using only a few hundreds of training data samples, while exhibiting the predictive power sufficient for reliable design. Our methodology is demonstrated by re-using the presented surrogate for the design of high-performance transmitarrays operating at various frequency ranges of 8–14 GHz, 22–28 GHz, and 28–36 GHz.https://doi.org/10.1038/s41598-023-42134-w |
| spellingShingle | Mehmet A. Belen Alper Caliskan Slawomir Koziel Anna Pietrenko-Dabrowska Peyman Mahouti Optimal design of transmitarray antennas via low-cost surrogate modelling Scientific Reports |
| title | Optimal design of transmitarray antennas via low-cost surrogate modelling |
| title_full | Optimal design of transmitarray antennas via low-cost surrogate modelling |
| title_fullStr | Optimal design of transmitarray antennas via low-cost surrogate modelling |
| title_full_unstemmed | Optimal design of transmitarray antennas via low-cost surrogate modelling |
| title_short | Optimal design of transmitarray antennas via low-cost surrogate modelling |
| title_sort | optimal design of transmitarray antennas via low cost surrogate modelling |
| url | https://doi.org/10.1038/s41598-023-42134-w |
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