Deep reinforcement learning-based random access method for low earth orbit satellite network and terminal for the operation
US11832314B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 12, 2022 |
| Grant date | Nov 28, 2023 |
| Priority date | — |
| Expiry date | Apr 12, 2042 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04W84/06
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
A random access method for a terminal with the processor and the memory to access a low earth orbit satellite network formed by multiple low earth orbit satellites (LEO SAT) includes: the stage where a Deep Reinforcement Learning (DRL) algorithm is applied for a pre-set time to decide which one between the first and the second actions should be performed at every access cycle, and to perform the random access to the low earth orbit satellite network based on the above decision while learning it; and the stage where, according to the learning result of the DRL algorithm performed for above pre-set time, it decides which of the first and the second actions should be chosen when attempting to access the low earth orbit satellite network at a new access cycle and then to perform the random access to the low earth orbit satellite network according to the above choice.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.