Telefonaktiebolaget Lm Ericsson (Publ)

Raul Martin Cuerdo

Philipp Frank

Chin Lam Eng

Mitchell Ho

Chee Wai Ng

Jaime Rodriguez Membrive

Javier Rasines

Bhavika Reddy Jalli

Michal Horemuz

Serene Banerjee

Shweta Ramesh

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

The invention refers to method, performed by a network optimization function associated with a wireless network (), of forecasting performance issues associated with cells () in the wireless network, the method comprising: for each of a plurality of cells in the wireless network, obtaining () first time-series data representing a plurality of key performance indicators, KPIs, at a first plurality of past time points; applying () a multi-layer forecasting model to the first time-series data to determine second time-series data representing the plurality of KPIs for each of the plurality of cells at a second plurality of future time points; and based on the second time-series data, determining () respective probabilities of occurrence for a respective plurality of different performance issues, associated with the plurality of cells, during one or more future time periods that include the second plurality of future time points; the method further refers to a corresponding network optimization function, a network optimization node and corresponding software.

Method for cell issue forecasting

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

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MADRID, Spain

Raul Martin Cuerdo