Deep Reinforcement Learning-based Multi-Channel Access for Industrial Wireless Networks with Dynamic Multi-User Priority | |
Liu XY(刘晓宇)1,2,3,4; Xu C(许驰)2,3,4; Yu HB(于海斌)2,3,4; Zeng P(曾鹏)2,3,4 | |
刊名 | IEEE Transactions on Industrial Informatics |
2021 | |
页码 | 1-11 |
关键词 | Deep reinforcement learning industrial wireless networks multi-channel access dynamic priority quality of service |
ISSN号 | 1551-3203 |
产权排序 | 1 |
英文摘要 | To address the high concurrent access of massive industrial devices with different QoS requirements for industrial wireless networks, a Deep Reinforcement Learning-based Dynamic Priority Multi-Channel Access (DRL-DPMCA) algorithm is proposed. According to the time-sensitivity of data, industrial devices are assigned with different priorities, based on which their channel access probabilities are adjusted. The Markov decision process is utilized to model the above problem. Because of the explosion of state space, DRL is used to map from states to actions. The long-term cumulative reward is maximized to obtain an effective policy. A compound reward with access reward and priority reward is designed. An experience replay with experience-weight is proposed. GRU, dueling architecture and step-by-step epsilon-greedy method are employed. Experiments show that, comparing with slotted-Aloha and DQN, DRL-DPMCA converges quickly, and guarantees the highest channel access probability and the minimum queuing delay for high-priority industrial devices in the context of minimum access conflicts. |
语种 | 英语 |
资助机构 | National Key Research and Development Program of China (2020YFB1710900) ; National Natural Science Foundation of China (62173322, 61803368, U1908212, 61821005) ; China Postdoctoral Science Foundation (2019M661156) ; Youth Innovation Promotion Association, Chinese Academy of Sciences (2019202) |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/30294] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Xu C(许驰); Yu HB(于海斌) |
作者单位 | 1.University of Chinese Academy of Sciences, Beijing 100049, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China |
推荐引用方式 GB/T 7714 | Liu XY,Xu C,Yu HB,et al. Deep Reinforcement Learning-based Multi-Channel Access for Industrial Wireless Networks with Dynamic Multi-User Priority[J]. IEEE Transactions on Industrial Informatics,2021:1-11. |
APA | Liu XY,Xu C,Yu HB,&Zeng P.(2021).Deep Reinforcement Learning-based Multi-Channel Access for Industrial Wireless Networks with Dynamic Multi-User Priority.IEEE Transactions on Industrial Informatics,1-11. |
MLA | Liu XY,et al."Deep Reinforcement Learning-based Multi-Channel Access for Industrial Wireless Networks with Dynamic Multi-User Priority".IEEE Transactions on Industrial Informatics (2021):1-11. |
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