Classifying RF interference sources using machine learning and operations, administration, and management data

Classifying RF interference sources using machine learning and operations, administration, and management data

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 prov...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Gormley, Eamonn, Valdes Valdes, Jesus Alejandro, Yun, Jungnam
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC COMMUNICATION TECHNIQUE
ELECTRICITY
PHYSICS
TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHICCOMMUNICATION
WIRELESS COMMUNICATIONS NETWORKS
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Beschreibung
Zusammenfassung: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.