公开(公告)号：US12035380B2

公开(公告)日：2024-07-09

申请号：US17296509

申请日：2020-12-25

Applicant: SHENYANG INSTITUTE OF AUTOMATION, CHINESE ACADEMY OF SCIENCES

Inventor： Haibin Yu ,  Xiaoyu Liu ,  Chi Xu ,  Peng Zeng ,  Xi Jin ,  Changqing Xia

IPC: H04W74/08 ,  G06N3/04 ,  G06N3/045 ,  H04L5/00 ,  H04W74/00

CPC classification number: H04W74/0875 ,  G06N3/045 ,  H04L5/003 ,  H04W74/002

Abstract: An industrial 5G dynamic multi-priority multi-access method based on deep reinforcement learning includes the following steps: establishing an industrial 5G network model; establishing a dynamic multi-priority multi-channel access neural network model based on deep reinforcement learning; collecting state, action and reward information of multiple time slots of all industrial 5G terminals in the industrial 5G network as training data; training the neural network model by using the collected data until the packet loss ratio and end-to-end latency meet industrial communication requirements; collecting the state information of all the industrial 5G terminals in the industrial 5G network at the current time slot as the input of the neural network model; conducting multi-priority channel allocation; and conducting multi-access by the industrial 5G terminals according to a channel allocation result. The method allocates multiple channels to the industrial 5G terminals of different priorities in the industrial 5G network in real time to ensure large-scale concurrent access.