Telefonaktiebolaget Lm Ericsson (publ)

Daniel Wilson

Selim Ickin

Ravi Kiran Kotty

Lackis Eleftheriadis

Leonid Mokrushin

Abhishek Sarkar

Kaushik Dey

Amos Kao

Barjinder Kochar

David Fernandez

Varun Juyal

The disclosure provides methods, apparatus and machine-readable mediums for the detection of sleeping cells in a cellular network. A method of detecting a sleeping cell in a cellular communication network comprises: monitoring power consumption of a radio transmission point of the cellular communication network; providing the power consumption of the radio transmission point as an input to a classification model, developed using a machine-learning algorithm; and obtaining an output from the classification model, the output classifying the radio transmission point as serving one of a sleeping cell and a non-sleeping cell.

Methods, apparatus and computer-readable mediums relating to detection of sleeping cells in a cellular network

A method and system for predicting a state of a cell in a radio access network are described herein. The method includes obtaining information of the cell, determining one or more sets of conditions based on the information and predicting that the cell will enter a sleeping state when at least one set of the one or more sets of conditions is fulfilled. The method further includes outputting an action to prevent the cell from entering the sleeping state based on the probability and a number of wireless devices currently connected to the cell.

Method and system for predicting a state of a cell in radio access network

Managing network event data in a telecommunication network. The method includes obtaining a plurality of datasets having network event data associated with products provided by a different vendor, the network event data comprising a plurality of data instances representing a plurality of network event features. The method further comprises obtaining metadata describing the network event data in the datasets and generating feature description vectors from the obtained metadata, calculating a metric of the relation between network event features represented in the datasets and constructing a graph of the network event features represented in the datasets, with edges weighted according to the calculated metric, partitioning the graph into clusters by minimising an edge cut between network event features, labelling network event features in the cluster with a normalised network event feature index and inputting the labelled network event data to a model for making at least one of recommendations or predictions.

Managing telecommunication network event data

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Jackson, NJ, United States of America

Daniel Wilson