Radio access network control with deep reinforcement learning
US11494649B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 31, 2020 |
| Grant date | Nov 8, 2022 |
| Priority date | — |
| Expiry date | May 8, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04W84/042
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A processing system including at least one processor may obtain operational data from a radio access network (RAN), format the operational data into state information and reward information for a reinforcement learning agent (RLA), processing the state information and the reward information via the RLA, where the RLA comprises a plurality of sub-agents, each comprising a respective neural network, each of the neural networks encoding a respective policy for selecting at least one setting of at least one parameter of the RAN to increase a respective predicted reward in accordance with the state information, and where each neural network is updated in accordance with the reward information. The processing system may further determine settings for parameters of the RAN via the RLA, where the RLA determines the settings in accordance with selections for the settings via the plurality of sub-agents, and apply the plurality of settings to the RAN.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.