Telefonaktiebolaget Lm Ericsson (publ)

Amy De Buitléir

Mahmood Osorio

Eugene Gomes

Premnath Kandhasamy Narayanan

James O'Meara

Donald Stephen Staudte

Diego Martos

Armagan Uskudar

A method performed by a node in a communications network for predicting energy usage in the communications network. The method comprises obtaining values of network parameters for a plurality of nodes in the communications network and corresponding energy usage measurements for the plurality of nodes, and obtaining a subset of the network parameters that are correlated with the energy usage measurements. The method then comprises training a first model using a first machine learning process to take as input the values of the subset of network parameters for the nodes in the plurality of nodes, and output a prediction of the corresponding energy usage measurements.

Energy usage in a communications network

A method for controlling operation of a communications network based on predicting a value of a first Key Performance Indicator, KPI. The method comprises maintaining () a plurality of machine-learning processes, wherein an individual machine-learning process operates based on a data model and decision-making rules making up a predicting logic of said individual machine-learning process. The plurality 10 of machine-learning processes operate based on a plurality of different data models and a plurality of different decision-making rules. The method further comprises receiving () a stream of values of Key Performance Indicators, KPIs, obtained from network elements of the communications network and predicting () a plurality of future values of said first KPI, wherein the future values of said first KPI refer to the same point in 15 time. Further the method comprises combining () the individual predictions into a single predicted value of the first KPI and triggering () a network management operation if the single predicted first KPI value is equal to or exceeds a first threshold ().

Method and apparatus for key performance indicator forecasting using artificial life

A method for recommending configuration changes in a communications network. The method comprises maintaining () a plurality of machine-learning processes, wherein an individual machine-learning process operates based on a data model and decision-making rules, and the plurality of machine-learning processes operate based on a plurality of different data models and a plurality of different decision-making rules. The method also comprises obtaining () values of Key Performance Indicators, KPIs, from network elements of the communications network and obtaining a goal () defining at least one KPI value characterising wanted operation of the communications network. The method also comprises producing () by the plurality of machine-learning processes, based on the received values of KPIs and using the data models and decision-making rules, a plurality of individual recommendations; and producing an output recommendation () based on the produced individual recommendations. An apparatus realising the above method is also disclosed.

Method and apparatus for dynamic network configuration and optimisation using artificial life

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Amy De Buitléir