Artificial Intelligence Based Mobile Tracking and Antenna Pointing in Satellite-Terrestrial Network
In recent years, mobile services have developed rapidly and traditional satellite-terrestrial networks have been unable to support them. We are faced with the problems of how to locate mobile terminals accurately and process the data we collected quickly to reduce communication pressure. In order to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8917634/ |
| _version_ | 1818875912149532672 |
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| author | Qiang Liu Jun Yang Chaojian Zhuang Ahmed Barnawi Bander A Alzahrani |
| author_facet | Qiang Liu Jun Yang Chaojian Zhuang Ahmed Barnawi Bander A Alzahrani |
| author_sort | Qiang Liu |
| collection | DOAJ |
| description | In recent years, mobile services have developed rapidly and traditional satellite-terrestrial networks have been unable to support them. We are faced with the problems of how to locate mobile terminals accurately and process the data we collected quickly to reduce communication pressure. In order to solve this problem, this paper studies a pointing and tracking method based on artificial intelligence for mobile stations and terminals in satellite-terrestrial network, to make sure that our mobile stations and terminals can access best antenna signal and suffer minimal communication interference from other stations or terminals. An AI-based self learning (ASL) network framework is designed to support filtering and correct original sampling data, mobile tracking of mobile stations and terminals, and unsupervised satellite selection and antenna adjustment scheme. Deep learning of historical information data of stations and terminals to achieve real-time pointing and tracking, and predict the distribution of stations and terminals at some time in the future. Finally, the ASL is compared with existing systems to measure their functionality and usability. |
| first_indexed | 2024-12-19T13:34:02Z |
| format | Article |
| id | doaj.art-539bfbe050314d11a2c66a7eb6c2395a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:34:02Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-539bfbe050314d11a2c66a7eb6c2395a2022-12-21T20:19:16ZengIEEEIEEE Access2169-35362019-01-01717749717750310.1109/ACCESS.2019.29565448917634Artificial Intelligence Based Mobile Tracking and Antenna Pointing in Satellite-Terrestrial NetworkQiang Liu0https://orcid.org/0000-0002-4425-7903Jun Yang1https://orcid.org/0000-0003-1921-7915Chaojian Zhuang2https://orcid.org/0000-0003-1568-7215Ahmed Barnawi3https://orcid.org/0000-0003-0516-8331Bander A Alzahrani4Beijing Key Laboratory of Transportation Data Analysis and Mining, Beijing Jiaotong University, Beijing, ChinaSchool of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, ChinaFaculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi ArabiaFaculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi ArabiaIn recent years, mobile services have developed rapidly and traditional satellite-terrestrial networks have been unable to support them. We are faced with the problems of how to locate mobile terminals accurately and process the data we collected quickly to reduce communication pressure. In order to solve this problem, this paper studies a pointing and tracking method based on artificial intelligence for mobile stations and terminals in satellite-terrestrial network, to make sure that our mobile stations and terminals can access best antenna signal and suffer minimal communication interference from other stations or terminals. An AI-based self learning (ASL) network framework is designed to support filtering and correct original sampling data, mobile tracking of mobile stations and terminals, and unsupervised satellite selection and antenna adjustment scheme. Deep learning of historical information data of stations and terminals to achieve real-time pointing and tracking, and predict the distribution of stations and terminals at some time in the future. Finally, the ASL is compared with existing systems to measure their functionality and usability.https://ieeexplore.ieee.org/document/8917634/Satellite-terrestrial networkartificial intelligencemobile trackingdeep learningunsupervised self learning |
| spellingShingle | Qiang Liu Jun Yang Chaojian Zhuang Ahmed Barnawi Bander A Alzahrani Artificial Intelligence Based Mobile Tracking and Antenna Pointing in Satellite-Terrestrial Network IEEE Access Satellite-terrestrial network artificial intelligence mobile tracking deep learning unsupervised self learning |
| title | Artificial Intelligence Based Mobile Tracking and Antenna Pointing in Satellite-Terrestrial Network |
| title_full | Artificial Intelligence Based Mobile Tracking and Antenna Pointing in Satellite-Terrestrial Network |
| title_fullStr | Artificial Intelligence Based Mobile Tracking and Antenna Pointing in Satellite-Terrestrial Network |
| title_full_unstemmed | Artificial Intelligence Based Mobile Tracking and Antenna Pointing in Satellite-Terrestrial Network |
| title_short | Artificial Intelligence Based Mobile Tracking and Antenna Pointing in Satellite-Terrestrial Network |
| title_sort | artificial intelligence based mobile tracking and antenna pointing in satellite terrestrial network |
| topic | Satellite-terrestrial network artificial intelligence mobile tracking deep learning unsupervised self learning |
| url | https://ieeexplore.ieee.org/document/8917634/ |
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