Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mapping
Abstract This paper presents a systematic antenna design methodology that integrates machine learning, leveraging the progressive growth technique of Progressive Growing of GANs (PGGAN) to generate images of various dual‐band PIFA‐like antenna structures. The process involves using data augmentation...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Microwaves, Antennas & Propagation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/mia2.12534 |
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| _version_ | 1850132473099321344 |
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| author | Lung‐Fai Tuen Ching‐Lieh Li Yu‐Jen Chi Chien‐Ching Chiu Po Hsiang Chen |
| author_facet | Lung‐Fai Tuen Ching‐Lieh Li Yu‐Jen Chi Chien‐Ching Chiu Po Hsiang Chen |
| author_sort | Lung‐Fai Tuen |
| collection | DOAJ |
| description | Abstract This paper presents a systematic antenna design methodology that integrates machine learning, leveraging the progressive growth technique of Progressive Growing of GANs (PGGAN) to generate images of various dual‐band PIFA‐like antenna structures. The process involves using data augmentation methods to generate 4180 antenna samples. In the latent space, the authors employ Latin Hypercube Sampling to maintain diversity and combine it with the Hough Transform to enhance the edge features of the antennas while providing labelling functionality. This labelling method strengthens the relationship between antenna frequency and wavelength characteristics. The paper clearly outlines the design process, starting from the simulation of two types of single‐frequency PIFA‐like antennas (2.45 and 5.2 GHz, respectively) to the completion of PGGAN's generation task, resulting in a novel dual‐band Wi‐Fi PIFA‐like antenna structure. The measurement results of the dual‐band antennas exhibit excellent consistency with the simulation results. |
| format | Article |
| id | doaj-art-de5c9c6dff6c43a69bf208f790f80dd6 |
| institution | OA Journals |
| issn | 1751-8725 1751-8733 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Microwaves, Antennas & Propagation |
| spelling | doaj-art-de5c9c6dff6c43a69bf208f790f80dd62025-08-20T02:32:12ZengWileyIET Microwaves, Antennas & Propagation1751-87251751-87332024-12-0118121113113810.1049/mia2.12534Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mappingLung‐Fai Tuen0Ching‐Lieh Li1Yu‐Jen Chi2Chien‐Ching Chiu3Po Hsiang Chen4Department of Electrical and Computer Engineering Tamkang University New Taipei City TaiwanDepartment of Electrical and Computer Engineering Tamkang University New Taipei City TaiwanDepartment of Electrical and Computer Engineering Tamkang University New Taipei City TaiwanDepartment of Electrical and Computer Engineering Tamkang University New Taipei City TaiwanDepartment of Electrical and Computer Engineering Tamkang University New Taipei City TaiwanAbstract This paper presents a systematic antenna design methodology that integrates machine learning, leveraging the progressive growth technique of Progressive Growing of GANs (PGGAN) to generate images of various dual‐band PIFA‐like antenna structures. The process involves using data augmentation methods to generate 4180 antenna samples. In the latent space, the authors employ Latin Hypercube Sampling to maintain diversity and combine it with the Hough Transform to enhance the edge features of the antennas while providing labelling functionality. This labelling method strengthens the relationship between antenna frequency and wavelength characteristics. The paper clearly outlines the design process, starting from the simulation of two types of single‐frequency PIFA‐like antennas (2.45 and 5.2 GHz, respectively) to the completion of PGGAN's generation task, resulting in a novel dual‐band Wi‐Fi PIFA‐like antenna structure. The measurement results of the dual‐band antennas exhibit excellent consistency with the simulation results.https://doi.org/10.1049/mia2.12534dual‐band antennahough transformLatin hypercube samplingPGGANWGAN‐GP |
| spellingShingle | Lung‐Fai Tuen Ching‐Lieh Li Yu‐Jen Chi Chien‐Ching Chiu Po Hsiang Chen Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mapping IET Microwaves, Antennas & Propagation dual‐band antenna hough transform Latin hypercube sampling PGGAN WGAN‐GP |
| title | Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mapping |
| title_full | Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mapping |
| title_fullStr | Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mapping |
| title_full_unstemmed | Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mapping |
| title_short | Machine learning‐driven design of dual‐band antennas using PGGAN and enhanced feature mapping |
| title_sort | machine learning driven design of dual band antennas using pggan and enhanced feature mapping |
| topic | dual‐band antenna hough transform Latin hypercube sampling PGGAN WGAN‐GP |
| url | https://doi.org/10.1049/mia2.12534 |
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