Dynamic optimization of intersatellite link assignment based on reinforcement learning
Intersatellite links can reduce the dependence of satellite communication systems on ground networks, reduce the number of ground gateways, and reduce the complexity and investment of ground networks, which are important future trends in satellite development. Intersatellite links are dynamic over t...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-02-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477211070202 |
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| _version_ | 1849396464274374656 |
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| author | Weiwu Ren Jialin Zhu Hui Qi Ligang Cong Xiaoqiang Di |
| author_facet | Weiwu Ren Jialin Zhu Hui Qi Ligang Cong Xiaoqiang Di |
| author_sort | Weiwu Ren |
| collection | DOAJ |
| description | Intersatellite links can reduce the dependence of satellite communication systems on ground networks, reduce the number of ground gateways, and reduce the complexity and investment of ground networks, which are important future trends in satellite development. Intersatellite links are dynamic over time, and different intersatellite topologies have a great impact on satellite network performance. To improve the overall performance of satellite networks, a satellite link assignment optimization algorithm based on reinforcement learning is proposed in this article. Different from the swarm intelligence method in principle, this algorithm models the combinatorial optimization problem of links as the optimal sequence decision problem of a series of link selection actions. Realistic constraints such as intersatellite visibility, network connectivity, and number of antenna beams are regarded as fully observable environmental factors. The agent selects the link according to the decision, and the selection action utility affects the next selection decision. After a finite number of iterations, the optimal link assignment scheme with minimum link delay is achieved. The simulation results show that in 8 or 12 satellite network systems, compared with the original topology, the topology calculated by this method has better network delay and smaller delay variance. |
| format | Article |
| id | doaj-art-b341744af22940d3b4db8b1efd3d58d8 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-b341744af22940d3b4db8b1efd3d58d82025-08-20T03:39:19ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-02-011810.1177/15501477211070202Dynamic optimization of intersatellite link assignment based on reinforcement learningWeiwu RenJialin ZhuHui QiLigang CongXiaoqiang DiIntersatellite links can reduce the dependence of satellite communication systems on ground networks, reduce the number of ground gateways, and reduce the complexity and investment of ground networks, which are important future trends in satellite development. Intersatellite links are dynamic over time, and different intersatellite topologies have a great impact on satellite network performance. To improve the overall performance of satellite networks, a satellite link assignment optimization algorithm based on reinforcement learning is proposed in this article. Different from the swarm intelligence method in principle, this algorithm models the combinatorial optimization problem of links as the optimal sequence decision problem of a series of link selection actions. Realistic constraints such as intersatellite visibility, network connectivity, and number of antenna beams are regarded as fully observable environmental factors. The agent selects the link according to the decision, and the selection action utility affects the next selection decision. After a finite number of iterations, the optimal link assignment scheme with minimum link delay is achieved. The simulation results show that in 8 or 12 satellite network systems, compared with the original topology, the topology calculated by this method has better network delay and smaller delay variance.https://doi.org/10.1177/15501477211070202 |
| spellingShingle | Weiwu Ren Jialin Zhu Hui Qi Ligang Cong Xiaoqiang Di Dynamic optimization of intersatellite link assignment based on reinforcement learning International Journal of Distributed Sensor Networks |
| title | Dynamic optimization of intersatellite link assignment based on reinforcement learning |
| title_full | Dynamic optimization of intersatellite link assignment based on reinforcement learning |
| title_fullStr | Dynamic optimization of intersatellite link assignment based on reinforcement learning |
| title_full_unstemmed | Dynamic optimization of intersatellite link assignment based on reinforcement learning |
| title_short | Dynamic optimization of intersatellite link assignment based on reinforcement learning |
| title_sort | dynamic optimization of intersatellite link assignment based on reinforcement learning |
| url | https://doi.org/10.1177/15501477211070202 |
| work_keys_str_mv | AT weiwuren dynamicoptimizationofintersatellitelinkassignmentbasedonreinforcementlearning AT jialinzhu dynamicoptimizationofintersatellitelinkassignmentbasedonreinforcementlearning AT huiqi dynamicoptimizationofintersatellitelinkassignmentbasedonreinforcementlearning AT ligangcong dynamicoptimizationofintersatellitelinkassignmentbasedonreinforcementlearning AT xiaoqiangdi dynamicoptimizationofintersatellitelinkassignmentbasedonreinforcementlearning |