Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions
The unmanned swarm system (USS) has been seen as a promising technology, and will play an extremely important role in both the military and civilian fields such as military strikes, disaster relief and transportation business. As the “nerve center” of USS, the unmanned swarm co...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9091841/ |
| _version_ | 1818624254199988224 |
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| author | Ximing Wang Yuhua Xu Chaohui Chen Xiaoqin Yang Jiaxin Chen Lang Ruan Yifan Xu Runfeng Chen |
| author_facet | Ximing Wang Yuhua Xu Chaohui Chen Xiaoqin Yang Jiaxin Chen Lang Ruan Yifan Xu Runfeng Chen |
| author_sort | Ximing Wang |
| collection | DOAJ |
| description | The unmanned swarm system (USS) has been seen as a promising technology, and will play an extremely important role in both the military and civilian fields such as military strikes, disaster relief and transportation business. As the “nerve center” of USS, the unmanned swarm communication system (USCS) provides the necessary information transmission medium so as to ensure the system stability and mission implementation. However, challenges caused by multiple tasks, distributed collaboration, high dynamics, ultra-dense and jamming threat make it hard for USCS to manage limited spectrum resources. To tackle with such problems, the machine learning (ML) empowered intelligent spectrum management technique is introduced in this paper. First, based on the challenges of the spectrum resource management in USCS, the requirement of spectrum sharing is analyzed from the perspective of spectrum collaboration and spectrum confrontation. We found that suitable multi-agent collaborative decision making is promising to realize effective spectrum sharing in both two perspectives. Therefore, a multi-agent learning framework is proposed which contains mobile-computing-assisted and distributed structures. Based on the framework, we provide case studies. Finally, future research directions are discussed. |
| first_indexed | 2024-12-16T18:54:02Z |
| format | Article |
| id | doaj.art-36d47189a3644f38a6e1b06780b7a0e6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T18:54:02Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-36d47189a3644f38a6e1b06780b7a0e62022-12-21T22:20:36ZengIEEEIEEE Access2169-35362020-01-018898398984910.1109/ACCESS.2020.29941989091841Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and SolutionsXiming Wang0https://orcid.org/0000-0003-2216-9352Yuhua Xu1https://orcid.org/0000-0002-4930-940XChaohui Chen2https://orcid.org/0000-0001-5040-3893Xiaoqin Yang3https://orcid.org/0000-0003-1073-2195Jiaxin Chen4https://orcid.org/0000-0002-7375-0182Lang Ruan5https://orcid.org/0000-0002-3262-8623Yifan Xu6https://orcid.org/0000-0002-6031-3717Runfeng Chen7https://orcid.org/0000-0003-1644-192XCollege of Communications Engineering, Army Engineering University, Nanjing, ChinaCollege of Communications Engineering, Army Engineering University, Nanjing, ChinaGuangzhou Haige Communications Group Incorporated Company, Guangzhou, ChinaCollege of Communications Engineering, Army Engineering University, Nanjing, ChinaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, ChinaCollege of Communications Engineering, Army Engineering University, Nanjing, ChinaCollege of Communications Engineering, Army Engineering University, Nanjing, ChinaCollege of Communications Engineering, Army Engineering University, Nanjing, ChinaThe unmanned swarm system (USS) has been seen as a promising technology, and will play an extremely important role in both the military and civilian fields such as military strikes, disaster relief and transportation business. As the “nerve center” of USS, the unmanned swarm communication system (USCS) provides the necessary information transmission medium so as to ensure the system stability and mission implementation. However, challenges caused by multiple tasks, distributed collaboration, high dynamics, ultra-dense and jamming threat make it hard for USCS to manage limited spectrum resources. To tackle with such problems, the machine learning (ML) empowered intelligent spectrum management technique is introduced in this paper. First, based on the challenges of the spectrum resource management in USCS, the requirement of spectrum sharing is analyzed from the perspective of spectrum collaboration and spectrum confrontation. We found that suitable multi-agent collaborative decision making is promising to realize effective spectrum sharing in both two perspectives. Therefore, a multi-agent learning framework is proposed which contains mobile-computing-assisted and distributed structures. Based on the framework, we provide case studies. Finally, future research directions are discussed.https://ieeexplore.ieee.org/document/9091841/Unmanned swarm systemspectrum sharingmachine learningmulti-agent learninggame theory |
| spellingShingle | Ximing Wang Yuhua Xu Chaohui Chen Xiaoqin Yang Jiaxin Chen Lang Ruan Yifan Xu Runfeng Chen Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions IEEE Access Unmanned swarm system spectrum sharing machine learning multi-agent learning game theory |
| title | Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions |
| title_full | Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions |
| title_fullStr | Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions |
| title_full_unstemmed | Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions |
| title_short | Machine Learning Empowered Spectrum Sharing in Intelligent Unmanned Swarm Communication Systems: Challenges, Requirements and Solutions |
| title_sort | machine learning empowered spectrum sharing in intelligent unmanned swarm communication systems challenges requirements and solutions |
| topic | Unmanned swarm system spectrum sharing machine learning multi-agent learning game theory |
| url | https://ieeexplore.ieee.org/document/9091841/ |
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