Context-aware learning-based access control method for power IoT
In view of the problems of severe access conflicts, high queue backlog, and low energy efficiency in the massive terminal access scenario of the power Internet of things (power IoT) in 6G era, a context-aware learning-based access control (CLAC) algorithm was proposed.The proposed algorithm was base...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2021-03-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021062/ |
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| author | Zhenyu ZHOU Zehan JIA Haijun LIAO Xiongwen ZHAO Lei ZHANG |
| author_facet | Zhenyu ZHOU Zehan JIA Haijun LIAO Xiongwen ZHAO Lei ZHANG |
| author_sort | Zhenyu ZHOU |
| collection | DOAJ |
| description | In view of the problems of severe access conflicts, high queue backlog, and low energy efficiency in the massive terminal access scenario of the power Internet of things (power IoT) in 6G era, a context-aware learning-based access control (CLAC) algorithm was proposed.The proposed algorithm was based on reinforcement learning and fast uplink grant technology, considering active state and dormant state of terminals, and the optimization objective was to maximize the total network energy efficiency under the long-term constraint of terminal access service quality requirements.Lyapunov optimization was used to decouple the long-term optimization objective and constraint, and the long-term optimization problem was transformed into a series of single time-slot independent deterministic sub-problems, which could be solved by the terminal state-aware upper confidence bound algorithm.The simulation results show that CLAC can improve the network energy efficiency while meeting the terminal access service quality requirements.Compared with the traditional fast uplink grant, CLAC can improve the average energy efficiency by 48.11%, increase the proportion of terminals meeting access service quality requirements by 54.95%, and reduce the average queue backlog by 83.83%. |
| format | Article |
| id | doaj-art-1e2d086f9def4379b90ea47feaaef71b |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2021-03-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-1e2d086f9def4379b90ea47feaaef71b2025-01-14T07:21:50ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2021-03-014215015959740949Context-aware learning-based access control method for power IoTZhenyu ZHOUZehan JIAHaijun LIAOXiongwen ZHAOLei ZHANGIn view of the problems of severe access conflicts, high queue backlog, and low energy efficiency in the massive terminal access scenario of the power Internet of things (power IoT) in 6G era, a context-aware learning-based access control (CLAC) algorithm was proposed.The proposed algorithm was based on reinforcement learning and fast uplink grant technology, considering active state and dormant state of terminals, and the optimization objective was to maximize the total network energy efficiency under the long-term constraint of terminal access service quality requirements.Lyapunov optimization was used to decouple the long-term optimization objective and constraint, and the long-term optimization problem was transformed into a series of single time-slot independent deterministic sub-problems, which could be solved by the terminal state-aware upper confidence bound algorithm.The simulation results show that CLAC can improve the network energy efficiency while meeting the terminal access service quality requirements.Compared with the traditional fast uplink grant, CLAC can improve the average energy efficiency by 48.11%, increase the proportion of terminals meeting access service quality requirements by 54.95%, and reduce the average queue backlog by 83.83%.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021062/6Gpower IoTmassive terminal accesscontext-aware learningfast uplink grant |
| spellingShingle | Zhenyu ZHOU Zehan JIA Haijun LIAO Xiongwen ZHAO Lei ZHANG Context-aware learning-based access control method for power IoT Tongxin xuebao 6G power IoT massive terminal access context-aware learning fast uplink grant |
| title | Context-aware learning-based access control method for power IoT |
| title_full | Context-aware learning-based access control method for power IoT |
| title_fullStr | Context-aware learning-based access control method for power IoT |
| title_full_unstemmed | Context-aware learning-based access control method for power IoT |
| title_short | Context-aware learning-based access control method for power IoT |
| title_sort | context aware learning based access control method for power iot |
| topic | 6G power IoT massive terminal access context-aware learning fast uplink grant |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2021062/ |
| work_keys_str_mv | AT zhenyuzhou contextawarelearningbasedaccesscontrolmethodforpoweriot AT zehanjia contextawarelearningbasedaccesscontrolmethodforpoweriot AT haijunliao contextawarelearningbasedaccesscontrolmethodforpoweriot AT xiongwenzhao contextawarelearningbasedaccesscontrolmethodforpoweriot AT leizhang contextawarelearningbasedaccesscontrolmethodforpoweriot |