Abstract: There is provided a method performed by a central entity of a network. A first set of features is selected for a machine learning model to take into account when analysing data. The machine learning model is to be deployed at an edge entity of the network. The selection is based on first information indicative of data that is available for the machine learning model to analyse, second information indicative of features that are available for the machine learning model to take into account when analysing data, and contextual information associated with the network.

Abstract: A method performed by a node in a communications network for determining a target network configuration for use in providing services to a first operator on the communications network. The method includes obtaining characteristics of the first operator; obtaining, for a plurality of previous operators in the communications network, characteristics of the previous operators and corresponding target network configurations used for the respective previous operators; matching the first operator to a second operator selected from the previous operators based on similarity between the characteristics of the first operator and the characteristics of the previous operators; and determining the target network configuration for the first operator, based on a target network configuration used for the second operator.

Abstract: A computer implemented method performed by a node in a communications network comprises obtaining Key Performance Indicator, KPI, targets for a plurality of KPIs, in the communications network, and determining relationships between the KPIs using a model trained using a graph-based machine learning process. Each relationship describes a manner in which changing a network configuration to alter a first one of the plurality of KPIs affects a second one of the plurality of KPIs. The method then comprises determining one or more conflicts between the KPI targets, using the relationships.

Abstract: There is provided a method for estimating a total energy consumption of a user equipment (UE) in a network. The method is performed by a network node. A total energy consumption for the UE is estimated (102) based on a resource usage for the UE and a measure of energy consumed by a base station of the network serving the UE in communicating with the UE. The resource usage for the UE is reported to the network node by the UE and/or the base station, and the measure of energy consumed by the base station is reported to the network node by the UE and/or the base station.

Abstract: Embodiments described herein relate to methods and apparatuses for training a neural network. A method comprises receiving an input data set at a layer of the neural network; performing a forward pass and a backward pass on the input data set to determine regular output data; calculating a first loss associated with the regular output data; performing a quantized forward pass and a quantized backward pass on the input data set to determine quantized output data; calculating a second loss associated with the quantized output data; comparing the first loss to the second loss; and based on the comparison determining whether to reduce the input data set to provide a reduced data set.

Abstract: A computer implemented method performed by a node in a communications network comprises obtaining Key Performance Indicator, KPI, targets for a plurality of KPIs, in the communications network, and determining relationships between the KPIs using a model trained using a graph-based machine learning process. Each relationship describes a manner in which changing a network configuration to alter a first one of the plurality of KPIs affects a second one of the plurality of KPIs. The method then comprises determining one or more conflicts between the KPI targets, using the relationships.

Abstract: Methods and sewer nodes generate machine learning models using models trained locally while avoiding misinformation by selectively aggregating models trained locally using data stored in client devices, which are connected to the server node via a communication network. The client devices receive an initial model and return updated model parameters of a respective model locally trained. Logical explanations are obtained, for each of the client devices, based on the updated model parameters and at least one set of input and corresponding output values. A distance based on the logical explanations, for each client device in a secondary cluster, measures a deviation of the respective model relative to model(s) of client devices in a primary cluster. The output model is generated by selectively aggregating at least the models received from the client devices in the primary cluster, while assessing each client device in the secondary cluster based on the distance thereof.

Abstract: The present disclosure relates to methods and devices (14, 20) for enabling mitigation of radio traffic overload in at least one radio cell (19) among a group of radio cells (17, 18, 19). In an aspect, a method of a supervising device (20) of enabling mitigation of radio traffic overload in at least one radio cell (19) among a group of radio cells (17, 18, 19) is provided.

Abstract: A method (200) performed by a node (100) in a telecommunications network for selecting a preferred action to take from a plurality of proposed actions in order to achieve a target network configuration in the telecommunications network. The method comprises obtaining (202) the plurality of proposed actions, evaluating (204) each of the plurality of proposed actions compared to the target network configuration using a computational argumentation process, and selecting (206) the preferred action, based on the results of the evaluating.