An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite Networks
Low Earth orbit (LEO) satellites that can be used as computing nodes are an important part of future communication networks. However, growing user demands, scarce channel resources and unstable satellite–ground links result in the challenge to design an efficient channel allocation algorithm for the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/10939 |
| _version_ | 1797469175281614848 |
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| author | Juan Wang Lijuan Sun Jian Zhou Chong Han |
| author_facet | Juan Wang Lijuan Sun Jian Zhou Chong Han |
| author_sort | Juan Wang |
| collection | DOAJ |
| description | Low Earth orbit (LEO) satellites that can be used as computing nodes are an important part of future communication networks. However, growing user demands, scarce channel resources and unstable satellite–ground links result in the challenge to design an efficient channel allocation algorithm for the LEO satellite network. Edge computing (EC) provides sufficient computing power for LEO satellite networks and makes the application of reinforcement learning possible. In this paper, an adaptive dynamic channel allocation algorithm based on a temporal–spatial correlation analysis for LEO satellite networks is proposed. First, according to the user mobility model, the temporal–spatial correlation of handoff calls is analyzed. Second, the dynamic channel allocation process in the LEO satellite network is formally described as a Markov decision process. Third, according to the temporal–spatial correlation, a policy for different call events is designed and online reinforcement learning is used to solve the channel allocation problem. Finally, the simulation results under different traffic distributions and different traffic intensities show that the proposed algorithm can greatly reduce the rejection probability of the handoff call and then improve the total performance of the LEO satellite network. |
| first_indexed | 2024-03-09T19:17:43Z |
| format | Article |
| id | doaj.art-07181f7aecbf493eae9911909498f47f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:17:43Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-07181f7aecbf493eae9911909498f47f2023-11-24T03:35:22ZengMDPI AGApplied Sciences2076-34172022-10-0112211093910.3390/app122110939An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite NetworksJuan Wang0Lijuan Sun1Jian Zhou2Chong Han3School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaLow Earth orbit (LEO) satellites that can be used as computing nodes are an important part of future communication networks. However, growing user demands, scarce channel resources and unstable satellite–ground links result in the challenge to design an efficient channel allocation algorithm for the LEO satellite network. Edge computing (EC) provides sufficient computing power for LEO satellite networks and makes the application of reinforcement learning possible. In this paper, an adaptive dynamic channel allocation algorithm based on a temporal–spatial correlation analysis for LEO satellite networks is proposed. First, according to the user mobility model, the temporal–spatial correlation of handoff calls is analyzed. Second, the dynamic channel allocation process in the LEO satellite network is formally described as a Markov decision process. Third, according to the temporal–spatial correlation, a policy for different call events is designed and online reinforcement learning is used to solve the channel allocation problem. Finally, the simulation results under different traffic distributions and different traffic intensities show that the proposed algorithm can greatly reduce the rejection probability of the handoff call and then improve the total performance of the LEO satellite network.https://www.mdpi.com/2076-3417/12/21/10939dynamic channel allocationtemporal–spatial correlation analysisLEO satellite networkreinforcement learningedge computing |
| spellingShingle | Juan Wang Lijuan Sun Jian Zhou Chong Han An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite Networks Applied Sciences dynamic channel allocation temporal–spatial correlation analysis LEO satellite network reinforcement learning edge computing |
| title | An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite Networks |
| title_full | An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite Networks |
| title_fullStr | An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite Networks |
| title_full_unstemmed | An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite Networks |
| title_short | An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite Networks |
| title_sort | adaptive dynamic channel allocation algorithm based on a temporal spatial correlation analysis for leo satellite networks |
| topic | dynamic channel allocation temporal–spatial correlation analysis LEO satellite network reinforcement learning edge computing |
| url | https://www.mdpi.com/2076-3417/12/21/10939 |
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