Detecting interference in a wireless network

Abstract

A methodis disclosed for generating and training a model to detect interference conditions at a cell in a wireless cellular network and to classify the impact of detected interference conditions on performance of the wireless cellular network in the cell. The method comprises, for each of a plurality of cells in the wireless cellular network (), obtaining data representing received signal power at a base station serving the cell over a period of time () and obtaining data representing a plurality of performance metrics for the cell over the time period (). The method further comprises obtaining classifications of the received signal power data into one of a plurality of cell interference conditions () and the performance metric data into one of a plurality of cell impact classes (). The method further comprises applying a Multi-Task Learning Machine Learning algorithm to a training data set comprising the classified received signal power and performance metric data to generate a model for classifying received signal power data into one of the plurality of cell interference conditions and for classifying performance metric data into one of the plurality of cell impact classes ().