Dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning
In recent years, with the increasing number of various emergency tasks, how to control the impact on common tasks while ensuring system revenue has become a huge challenge for the dynamic scheduling of relay satellite networks.Aiming at this problem, with the goal of maximizing the total revenue of...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2023-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023130/ |
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| author | Runzi LIU Tianci MA Weihua WU Chenhong YAO Qinghai YANG |
| author_facet | Runzi LIU Tianci MA Weihua WU Chenhong YAO Qinghai YANG |
| author_sort | Runzi LIU |
| collection | DOAJ |
| description | In recent years, with the increasing number of various emergency tasks, how to control the impact on common tasks while ensuring system revenue has become a huge challenge for the dynamic scheduling of relay satellite networks.Aiming at this problem, with the goal of maximizing the total revenue of emergency tasks and minimizing the damage to common tasks, a dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning was proposed.Specifically, in order to take into account the long-term and short-term performance of the system at the same time, a two-layer scheduling framework implemented by upper-level and lower-level DQN was designed.The upper-level DQN was responsible for determining the temporary optimization goal based on long-term performance, and the lower-level DQN determined the scheduling strategy for current task according to the optimization goal.Simulation results show that compared with traditional deep learning methods and the heuristic methods dealing with dynamic scheduling problems, the proposed method can improve the total revenue of urgent tasks while reducing the damage to common tasks. |
| format | Article |
| id | doaj-art-35e8893b27d84fc9bfb6bfc1461bf50a |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-35e8893b27d84fc9bfb6bfc1461bf50a2025-01-14T06:22:21ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-07-014420721759384197Dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learningRunzi LIUTianci MAWeihua WUChenhong YAOQinghai YANGIn recent years, with the increasing number of various emergency tasks, how to control the impact on common tasks while ensuring system revenue has become a huge challenge for the dynamic scheduling of relay satellite networks.Aiming at this problem, with the goal of maximizing the total revenue of emergency tasks and minimizing the damage to common tasks, a dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning was proposed.Specifically, in order to take into account the long-term and short-term performance of the system at the same time, a two-layer scheduling framework implemented by upper-level and lower-level DQN was designed.The upper-level DQN was responsible for determining the temporary optimization goal based on long-term performance, and the lower-level DQN determined the scheduling strategy for current task according to the optimization goal.Simulation results show that compared with traditional deep learning methods and the heuristic methods dealing with dynamic scheduling problems, the proposed method can improve the total revenue of urgent tasks while reducing the damage to common tasks.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023130/relay satellite networkstask schedulingdeep reinforcement learningmulti-objective optimizationdynamic scheduling |
| spellingShingle | Runzi LIU Tianci MA Weihua WU Chenhong YAO Qinghai YANG Dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning Tongxin xuebao relay satellite networks task scheduling deep reinforcement learning multi-objective optimization dynamic scheduling |
| title | Dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning |
| title_full | Dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning |
| title_fullStr | Dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning |
| title_full_unstemmed | Dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning |
| title_short | Dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning |
| title_sort | dynamic task scheduling method for relay satellite networks based on hierarchical reinforcement learning |
| topic | relay satellite networks task scheduling deep reinforcement learning multi-objective optimization dynamic scheduling |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023130/ |
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