Industrial 5G dynamic multi-priority multi-access method based on deep reinforcement learning
US12035380B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 25, 2020 |
| Grant date | Jul 9, 2024 |
| Priority date | — |
| Expiry date | Apr 15, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L5/0037
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.