Distributed interference coordination based on multi-agent deep reinforcement learning
A distributed interference coordination strategy based on multi-agent deep reinforcement learning was investigated to meet the requirements of file downloading traffic in interference networks.By the proposed strategy transmission scheme could be adjusted adaptively based on the interference environ...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2020-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020149/ |
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| _version_ | 1841539252498726912 |
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| author | Tingting LIU Yi’nan LUO Chenyang YANG |
| author_facet | Tingting LIU Yi’nan LUO Chenyang YANG |
| author_sort | Tingting LIU |
| collection | DOAJ |
| description | A distributed interference coordination strategy based on multi-agent deep reinforcement learning was investigated to meet the requirements of file downloading traffic in interference networks.By the proposed strategy transmission scheme could be adjusted adaptively based on the interference environment and traffic requirements with limited amount of information exchanged among nodes.Simulation results show that the user satisfaction loss of the proposed strategy from the optimal strategy with perfect future information does not exceed 11% for arbitrary number of users and traffic requirements. |
| format | Article |
| id | doaj-art-00ece809e0d54be9916ac924253fd18d |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-00ece809e0d54be9916ac924253fd18d2025-01-14T07:19:36ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-07-0141384859736432Distributed interference coordination based on multi-agent deep reinforcement learningTingting LIUYi’nan LUOChenyang YANGA distributed interference coordination strategy based on multi-agent deep reinforcement learning was investigated to meet the requirements of file downloading traffic in interference networks.By the proposed strategy transmission scheme could be adjusted adaptively based on the interference environment and traffic requirements with limited amount of information exchanged among nodes.Simulation results show that the user satisfaction loss of the proposed strategy from the optimal strategy with perfect future information does not exceed 11% for arbitrary number of users and traffic requirements.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020149/multi-agent deep reinforcement learningnon-realtime trafficdistributed interference coordinationultra-dense network |
| spellingShingle | Tingting LIU Yi’nan LUO Chenyang YANG Distributed interference coordination based on multi-agent deep reinforcement learning Tongxin xuebao multi-agent deep reinforcement learning non-realtime traffic distributed interference coordination ultra-dense network |
| title | Distributed interference coordination based on multi-agent deep reinforcement learning |
| title_full | Distributed interference coordination based on multi-agent deep reinforcement learning |
| title_fullStr | Distributed interference coordination based on multi-agent deep reinforcement learning |
| title_full_unstemmed | Distributed interference coordination based on multi-agent deep reinforcement learning |
| title_short | Distributed interference coordination based on multi-agent deep reinforcement learning |
| title_sort | distributed interference coordination based on multi agent deep reinforcement learning |
| topic | multi-agent deep reinforcement learning non-realtime traffic distributed interference coordination ultra-dense network |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020149/ |
| work_keys_str_mv | AT tingtingliu distributedinterferencecoordinationbasedonmultiagentdeepreinforcementlearning AT yinanluo distributedinterferencecoordinationbasedonmultiagentdeepreinforcementlearning AT chenyangyang distributedinterferencecoordinationbasedonmultiagentdeepreinforcementlearning |