Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices
Abstract Antenna design has evolved from bulkier to small portable designs but there is a need for smarter antenna design using machine learning algorithms that can meet today’s high growing demand for smart and fast devices. Here in this research, main focus is on developing smart antenna design us...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-16678-2 |
| _version_ | 1818192887644422144 |
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| author | Shobhit K. Patel Jaymit Surve Vijay Katkar Juveriya Parmar |
| author_facet | Shobhit K. Patel Jaymit Surve Vijay Katkar Juveriya Parmar |
| author_sort | Shobhit K. Patel |
| collection | DOAJ |
| description | Abstract Antenna design has evolved from bulkier to small portable designs but there is a need for smarter antenna design using machine learning algorithms that can meet today’s high growing demand for smart and fast devices. Here in this research, main focus is on developing smart antenna design using machine learning applicable in 5G mobile applications and portable Wi-Fi, Wi-MAX, and WLAN applications. Our design is based on the metamaterial concept where the patch is truncated and etched with a split ring resonator (SRR). The high gain requirement is met by adding metamaterial superstrates having thin wires (TW) and SRRs. The reconfigurability is achieved by adding three PIN diode switches. Multiple designs have been observed by adding superstrate layers ranging from one layer to four layers with interchanging TWs and SRRs. The TW metamaterial superstrate design with two layers is giving the best performance in gain, bandwidth, and the number of bands. The design is optimized by changing the path’s physical parameters. To shrink simulation time, Extra Tree Regression based machine learning model is used to learn the behavior of the antenna and predict the reflectance value for a wide range of frequencies. Experimental results prove that the use of the Extra Tree Regression based model for simulation of antenna design can cut the simulation time, resource requirements by 80%. |
| first_indexed | 2024-12-12T00:37:39Z |
| format | Article |
| id | doaj.art-542c05c4eadc41208ddd5fafe035cdba |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-12T00:37:39Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-542c05c4eadc41208ddd5fafe035cdba2022-12-22T00:44:20ZengNature PortfolioScientific Reports2045-23222022-07-0112111310.1038/s41598-022-16678-2Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devicesShobhit K. Patel0Jaymit Surve1Vijay Katkar2Juveriya Parmar3Department of Computer Engineering, Marwadi UniversityDepartment of Electrical Engineering, Marwadi UniversityDepartment of Computer Engineering, Marwadi UniversityDepartment of Electronics and Communication Engineering, Marwadi UniversityAbstract Antenna design has evolved from bulkier to small portable designs but there is a need for smarter antenna design using machine learning algorithms that can meet today’s high growing demand for smart and fast devices. Here in this research, main focus is on developing smart antenna design using machine learning applicable in 5G mobile applications and portable Wi-Fi, Wi-MAX, and WLAN applications. Our design is based on the metamaterial concept where the patch is truncated and etched with a split ring resonator (SRR). The high gain requirement is met by adding metamaterial superstrates having thin wires (TW) and SRRs. The reconfigurability is achieved by adding three PIN diode switches. Multiple designs have been observed by adding superstrate layers ranging from one layer to four layers with interchanging TWs and SRRs. The TW metamaterial superstrate design with two layers is giving the best performance in gain, bandwidth, and the number of bands. The design is optimized by changing the path’s physical parameters. To shrink simulation time, Extra Tree Regression based machine learning model is used to learn the behavior of the antenna and predict the reflectance value for a wide range of frequencies. Experimental results prove that the use of the Extra Tree Regression based model for simulation of antenna design can cut the simulation time, resource requirements by 80%.https://doi.org/10.1038/s41598-022-16678-2 |
| spellingShingle | Shobhit K. Patel Jaymit Surve Vijay Katkar Juveriya Parmar Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices Scientific Reports |
| title | Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices |
| title_full | Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices |
| title_fullStr | Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices |
| title_full_unstemmed | Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices |
| title_short | Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices |
| title_sort | machine learning assisted metamaterial based reconfigurable antenna for low cost portable electronic devices |
| url | https://doi.org/10.1038/s41598-022-16678-2 |
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