Classifying rf interference sources using machine learning and operations, administration, and management data
AssigneeSpectrum Effect Inc.
InventorsJungnam YUN, Eamonn Gormley, Jesus Alejandro Valdes Valdes
Life of this patent
Estimating the term…
Abstract
A method for classifying sources of interference provides Operations, Administration, and Management (OAM) data available in a wireless communication network to a trained machine learning model that outputs indications of the types of interference sources exhibited in the OAM data. The OAM data provided to the machine learning model may include per-Physical Resource Block (per-PRB) interference data for a cell, and may further include metadata corresponding to the configuration of the cell.
Claims20
A method for identifying a type of a source of interference received in a wireless telecommunications network, the method comprising:
- receiving Operations, Administration, and Management (OAM) data of the wireless telecommunications network;
- producing, using the OAM data, first per-Physical Resource Block (per-PRB) interference data for a cell of the wireless telecommunications network, the first per-PRB interference data including interference data for each of a plurality of PRBs of the cell for each of a plurality of intervals within a period of time;
- and
- producing, using a trained machine learning (ML) model and based on the first per-PRB interference data, one or more interference source type indications.
A network device comprising:
- at least one memory;
- and
- at least one processor coupled with the at least one memory and configured to cause the network device to:
- receive Operations, Administration, and Management (OAM) data of the wireless telecommunications network;
- produce, using the OAM data, first per-Physical Resource Block (per-PRB) interference data for a cell of the wireless telecommunications network, the first per-PRB interference data including interference data for each of a plurality of PRBs of the cell for each of a plurality of intervals within a period of time;
- and
- produce, using a trained machine learning (ML) model and based on the first per-PRB interference data, one or more interference source type indications.
Dependencies are inferred from the claim text.