Telefonaktiebolaget Lm Ericsson (publ)

Chee Wai Ng

Philipp Frank

Chin Lam Eng

Mitchell Ho

Raul Martin Cuerdo

Yu Jia

Lichao Gui

A method includes extracting, from first log data relating to operations of wireless devices and/or nodes in the communication network, textual elements and numerical elements; transforming the textual elements to a first vector space to determine respective textual element vectors; transforming the numerical elements to a second vector space to determine respective numerical element vectors; embedding and clustering the textual element vectors and the numerical element vectors to determine clusters of embedded vectors, wherein the embedding includes, for a plurality of wireless device sessions, embedding at least one textual element vector and at least one numerical element vector into a single embedded vector representing the particular wireless device session; and training a classifier model to determine a network status from second log data, wherein the classifier model is trained using the plurality of clusters of embedded vectors.

Classifier model for determining a network status of a communication network from log data

A method is provided for identifying operating conditions of a system. Input data relating to operation of the system is applied to a multi-class model for classification, where the multi-class model is configured for classifying the data into one of a plurality of predefined classes, and each class corresponds to a respective operating condition of the system. A confidence level of the classification by the multi-class model is determined. If the confidence level is below a threshold confidence level, the input data is applied to a plurality of binary models, where each binary model is configured for determining whether the data is or is not in a respective one of the predefined classes. If the plurality of binary models determine that the data is not in any of the respective predefined classes, the data can be taken into consideration when updating the multi-class model.

Life cycle management

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 ().

Detecting interference in a wireless network

The present invention refers to a method and apparatus and computer program for detecting communities in a radio access network with a plurality of vertices (C, BBU), wherein the method comprises calculating (S) relationship strengths () for network connections between the vertices (C, BBU) by executing a connection strength calculation process by analyzing performance factors, decisive for network performance including cell coverage overlap, signal strength, and inter-cell interference. The method further creates (S) a network graph with nodes, representing the vertices (C,BBU) in the network and edges, representing the network connections based on the calculated relationship strengths () and applies (S) an extended iterative disjoint community detection algorithm for clustering nodes into communities, wherein in each iteration imposed one or more constraints for clustering cooperating nodes into the same community are analyzed (S).

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Sydney, Australia

Chee Wai Ng