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	DEEP REINFORCEMENT LEARNING-BASED INDUSTRIAL 5G DYNAMIC MULTI-PRIORITY MULTI-ACCESS METHOD
	Yu HB(于海斌); Liu XY(刘晓宇); Xu C(许驰); Zeng P(曾鹏); Jin X(金曦); Xia CQ(夏长清)
	2021-11-18
著作权人	SHENYANG INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES
国家	世界专利组织
文献子类	发明
产权排序	1
其他题名	基于深度强化学习的工业5G动态多优先级多接入方法
英文摘要	The present invention relates to industrial 5G network technology, and specifically relates to a deep reinforcement learning-based industrial 5G dynamic multi-priority multi-access method. The method comprises the following steps: establishing an industrial 5G network model; establishing a deep reinforcement learning-based dynamic multi-priority multi-channel access neural network model; collecting status, action, and reward information of all industrial 5G terminals in the industrial 5G network in multiple time slots as training data; using the collected data to train the neural network model until the packet loss rate and end-to-end delay meet requirements for industrial communication; collecting status information of all industrial 5G terminals in the industrial 5G network in a current time slot and using same as an input of the neural network model for multi-priority channel allocation, and the industrial 5G terminals perform multiple access according to the channel allocation results. The present invention may efficiently perform multi-channel allocation with industrial 5G terminals of different priorities in an industrial 5G network in real time, and ensure large-scale concurrent access.
申请日期	2020-12-25
语种	英语
状态	公开
内容类型	专利
源URL	[http://ir.sia.cn/handle/173321/30139]
专题	沈阳自动化研究所_工业控制网络与系统研究室
作者单位	SHENYANG INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES
推荐引用方式 GB/T 7714	Yu HB,Liu XY,Xu C,et al. DEEP REINFORCEMENT LEARNING-BASED INDUSTRIAL 5G DYNAMIC MULTI-PRIORITY MULTI-ACCESS METHOD. 2021-11-18.

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