DEEP REINFORCEMENT LEARNING-BASED INDUSTRIAL 5G DYNAMIC MULTI-PRIORITY MULTI-ACCESS METHOD
文献类型：专利
| 作者 | 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)
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| 发表日期 | 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 resu...(前1000字) |
| 申请日期 | 2020-12-25 |
| 语种 | 英语 |
| 状态 | 公开 |
| 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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