Spectrum allocation algorithm based on multi-agent reinforcement learning in smart grid
In view of the fact that 5G networks are used to meet the service requirements of various power terminals in smart grid, a spectrum allocation algorithm based on multi-agent reinforcement learning was proposed.Firstly, for the integrated access backhaul system deployed in smart grid, 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
2023-09-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023179/ |
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| _version_ | 1841539246104510464 |
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| author | Feng YAN Xiaowei LIN Zhenghao LI Xia XU Weiwei XIA Lianfeng SHEN |
| author_facet | Feng YAN Xiaowei LIN Zhenghao LI Xia XU Weiwei XIA Lianfeng SHEN |
| author_sort | Feng YAN |
| collection | DOAJ |
| description | In view of the fact that 5G networks are used to meet the service requirements of various power terminals in smart grid, a spectrum allocation algorithm based on multi-agent reinforcement learning was proposed.Firstly, for the integrated access backhaul system deployed in smart grid, considering the different communication requirements of services in lightweight and non-lightweight terminal, the spectrum allocation problem was formulated as a non-convex mixed-integer programming aiming to maximize the overall energy efficiency.Secondly, the above problem was modeled as a partially observable Markov decision process and transformed into a fully cooperative multi-agent problem, then a spectrum allocation algorithm was proposed which was based on multi-agent proximal policy optimization under the framework of centralized training and distributed execution.Finally, the performance of the proposed algorithm was verified by simulation.The results show that the proposed algorithm has a faster convergence speed and can increase the overall transmission rate by 25.2% through effectively reducing intra-layer and inter-layer interference and balancing the access and backhaul link rates. |
| format | Article |
| id | doaj-art-ff306e1c657047009cc2e48db55614d2 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-09-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-ff306e1c657047009cc2e48db55614d22025-01-14T07:23:27ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-09-0144122459835747Spectrum allocation algorithm based on multi-agent reinforcement learning in smart gridFeng YANXiaowei LINZhenghao LIXia XUWeiwei XIALianfeng SHENIn view of the fact that 5G networks are used to meet the service requirements of various power terminals in smart grid, a spectrum allocation algorithm based on multi-agent reinforcement learning was proposed.Firstly, for the integrated access backhaul system deployed in smart grid, considering the different communication requirements of services in lightweight and non-lightweight terminal, the spectrum allocation problem was formulated as a non-convex mixed-integer programming aiming to maximize the overall energy efficiency.Secondly, the above problem was modeled as a partially observable Markov decision process and transformed into a fully cooperative multi-agent problem, then a spectrum allocation algorithm was proposed which was based on multi-agent proximal policy optimization under the framework of centralized training and distributed execution.Finally, the performance of the proposed algorithm was verified by simulation.The results show that the proposed algorithm has a faster convergence speed and can increase the overall transmission rate by 25.2% through effectively reducing intra-layer and inter-layer interference and balancing the access and backhaul link rates.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023179/smart gridintegrated access and backhaulspectrum allocationmulti-agent reinforcement learning |
| spellingShingle | Feng YAN Xiaowei LIN Zhenghao LI Xia XU Weiwei XIA Lianfeng SHEN Spectrum allocation algorithm based on multi-agent reinforcement learning in smart grid Tongxin xuebao smart grid integrated access and backhaul spectrum allocation multi-agent reinforcement learning |
| title | Spectrum allocation algorithm based on multi-agent reinforcement learning in smart grid |
| title_full | Spectrum allocation algorithm based on multi-agent reinforcement learning in smart grid |
| title_fullStr | Spectrum allocation algorithm based on multi-agent reinforcement learning in smart grid |
| title_full_unstemmed | Spectrum allocation algorithm based on multi-agent reinforcement learning in smart grid |
| title_short | Spectrum allocation algorithm based on multi-agent reinforcement learning in smart grid |
| title_sort | spectrum allocation algorithm based on multi agent reinforcement learning in smart grid |
| topic | smart grid integrated access and backhaul spectrum allocation multi-agent reinforcement learning |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023179/ |
| work_keys_str_mv | AT fengyan spectrumallocationalgorithmbasedonmultiagentreinforcementlearninginsmartgrid AT xiaoweilin spectrumallocationalgorithmbasedonmultiagentreinforcementlearninginsmartgrid AT zhenghaoli spectrumallocationalgorithmbasedonmultiagentreinforcementlearninginsmartgrid AT xiaxu spectrumallocationalgorithmbasedonmultiagentreinforcementlearninginsmartgrid AT weiweixia spectrumallocationalgorithmbasedonmultiagentreinforcementlearninginsmartgrid AT lianfengshen spectrumallocationalgorithmbasedonmultiagentreinforcementlearninginsmartgrid |