Improved satellite resource allocation algorithm based on DRL and MOP
In view of the multi-objective optimization (MOP) problem of sequential decision-making for resource allocations in multi-beam satellite systems,a deep reinforcement learning(DRL) based DRL-MOP algorithm was proposed to improve the system performance and user satisfaction degree.With considering the...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2020-06-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020117/ |
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| _version_ | 1841539373939556352 |
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| author | Pei ZHANG Shuaijun LIU Zhiguo MA Xiaohui WANG Junde SONG |
| author_facet | Pei ZHANG Shuaijun LIU Zhiguo MA Xiaohui WANG Junde SONG |
| author_sort | Pei ZHANG |
| collection | DOAJ |
| description | In view of the multi-objective optimization (MOP) problem of sequential decision-making for resource allocations in multi-beam satellite systems,a deep reinforcement learning(DRL) based DRL-MOP algorithm was proposed to improve the system performance and user satisfaction degree.With considering the normalized weighted sum of spectrum efficiency,energy efficiency,and satisfaction index as the optimization goal,the dynamically changing system environments and user arrival model were built by the proposed algorithm,and the optimization of the accumulative performance in satellite systems based on DRL and MOP was realized.Simulation results show that the proposed algorithm can solve the MOP problem with rapid convergence ability and low complexity,and it is obviously superior to other algorithms in terms of system performance and user satisfaction optimization. |
| format | Article |
| id | doaj-art-adb53244204b4b5ab906485d9417c591 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-06-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-adb53244204b4b5ab906485d9417c5912025-01-14T07:19:03ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-06-0141516059734705Improved satellite resource allocation algorithm based on DRL and MOPPei ZHANGShuaijun LIUZhiguo MAXiaohui WANGJunde SONGIn view of the multi-objective optimization (MOP) problem of sequential decision-making for resource allocations in multi-beam satellite systems,a deep reinforcement learning(DRL) based DRL-MOP algorithm was proposed to improve the system performance and user satisfaction degree.With considering the normalized weighted sum of spectrum efficiency,energy efficiency,and satisfaction index as the optimization goal,the dynamically changing system environments and user arrival model were built by the proposed algorithm,and the optimization of the accumulative performance in satellite systems based on DRL and MOP was realized.Simulation results show that the proposed algorithm can solve the MOP problem with rapid convergence ability and low complexity,and it is obviously superior to other algorithms in terms of system performance and user satisfaction optimization.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020117/multi-beam satellite systemresource allocationsequential decision-makingdeep reinforcement learningmulti-objective optimization |
| spellingShingle | Pei ZHANG Shuaijun LIU Zhiguo MA Xiaohui WANG Junde SONG Improved satellite resource allocation algorithm based on DRL and MOP Tongxin xuebao multi-beam satellite system resource allocation sequential decision-making deep reinforcement learning multi-objective optimization |
| title | Improved satellite resource allocation algorithm based on DRL and MOP |
| title_full | Improved satellite resource allocation algorithm based on DRL and MOP |
| title_fullStr | Improved satellite resource allocation algorithm based on DRL and MOP |
| title_full_unstemmed | Improved satellite resource allocation algorithm based on DRL and MOP |
| title_short | Improved satellite resource allocation algorithm based on DRL and MOP |
| title_sort | improved satellite resource allocation algorithm based on drl and mop |
| topic | multi-beam satellite system resource allocation sequential decision-making deep reinforcement learning multi-objective optimization |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020117/ |
| work_keys_str_mv | AT peizhang improvedsatelliteresourceallocationalgorithmbasedondrlandmop AT shuaijunliu improvedsatelliteresourceallocationalgorithmbasedondrlandmop AT zhiguoma improvedsatelliteresourceallocationalgorithmbasedondrlandmop AT xiaohuiwang improvedsatelliteresourceallocationalgorithmbasedondrlandmop AT jundesong improvedsatelliteresourceallocationalgorithmbasedondrlandmop |