Automatically optimize parameters via machine learning
US10785101B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 23, 2017 |
| Grant date | Sep 22, 2020 |
| Priority date | — |
| Expiry date | Dec 8, 2037 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04W24/08
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
The disclosure relates to technology for configuring parameters in a wireless communications network. Parameter configurations resulting in a change to key quality indicator (KQI) and key performance indicator (KPI) measurements are determined based on collected data samples. The data samples are divided into subsets including a first subset including the data samples associated with the parameter configurations failing to result in the change to the KQI and KPI measurements, and a second subset including the data samples associated with the parameter configurations resulting in the change to the KQI and KPI measurements dependent upon satisfying conditions in the wireless communications network. The subsets of the data samples are then determined for using machine learning to optimize the parameter configurations, and subsets of the data samples are provided as an input to machine learning for the parameter configurations to optimize the wireless communications network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.