Abstract: A computer-implemented method performed in a multi-agent system by a first network node is provided for transferring historical data from an operating agent to a second agent for an action controlling a performance of the multi-agent system. The method includes selecting at least one operating agent for transfer of historical data to the second agent. The historical data acquired from executions of an action by the at least one operating agent that at least partially fulfills an input parameter. The selecting is based on one or more criteria including (i) a performance of the at least one operating agent on the parameter or on a related parameter; (ii) an availability of the at least one operating agent; and (iii) an identity of an actuation target system for receipt of the action. The method further includes transferring the historical data to the second agent.

Abstract: Methods and systems for power supply unit (PSU) control. A method includes measuring one or more properties of the PSU to obtain property measurements, and initiating transmission of the property measurements to a machine learning (ML) agent hosting a trained ML model. The method further includes receiving the property measurements at the ML agent, and processing the received property measurements using the trained ML model to generate suggested actions to be taken by the PSU. The method further includes predicting the effect of each of the suggested actions on the measured PSU properties, and selecting a subset of the suggested actions predicted to have a significant impact on the measured PSU properties. The method further includes initiating transmission of the selected subset of suggested actions to the PSU, and performing, at the PSU, the selected subset of suggested actions.

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 is provided for determining bias of machine learning models. The method includes: forming a training dataset including input data samples provided to a remote machine learning model developed using a machine learning process, and corresponding output data samples obtained from the remote machine learning model; training a local machine learning model which approximates the remote machine learning model using a machine learning process and the training dataset; and interrogating the trained local machine learning model to determine whether the remote machine learning model is biased with respect to one or more biasing data parameters.

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 network node is provided. The method includes receiving a request for retrieving or executing a machine learning (ML) model or a combination of ML models. The request includes a first description of a specified output feature and specified input data type and distribution of input values for a ML model or combination of ML models. The method further includes obtaining an identification of a ML model, or a combination of ML models, having a second description that at least partially satisfies a match to the first description; identifying a candidate ML model, or combination of ML models, that produces the specified output feature of the first description based on a comparison of the first and second descriptions. The method further includes selecting a third description of the identified candidate ML model, or combination of ML models, based on a convergence.

Abstract: A method of operating a protection node for protecting a pattern classification node from malicious requests may be provided. The protection node may receive, from a user node, a request containing an original pattern to be classified by a machine learning algorithm performed by the pattern classification node. The protection node may add noise to the original pattern to generate a noisy pattern. The protection node may obtain a first classification of the noisy pattern based on processing of the noisy pattern by a first clone of the machine learning algorithm at the protection node; obtain a second classification of the original pattern based forwarding the request for processing of the original pattern by the machine learning algorithm performed at the pattern classification node; and compare the first and second classifications to determine whether the first and second classifications satisfy a defined similarity rule. The protection node may use the comparison to manage the request from the user node.

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: There is provided a method performed by an entity for managing connectivity of a device to a network. The method comprises selecting, from a plurality of connectivity service providers in the network, a connectivity service provider (CSP) to connect the device to the network. The selection is based on information about the device.

Abstract: A user device is configured to receive Hypertext Transfer Protocol, HTTP, requests sent by a user of the device. Information is obtained from the received HTTP requests about areas of interest to the user. The information obtained from the received HTTP requests is combined to generate a machine-generated representation of the areas of interest to the user. Then, data traffic sent to the user is received, and is modified based on the machine-generated representation of the areas of interest to the user.

Abstract: The present disclosure relates to a method of scheduling communication of a wireless communication device in a communications network, and a device performing the method. A method of a device of scheduling communication of a wireless communication device in a communications network is provided. The method includes determining a geographical path along which the wireless communication device is expected to travel, acquiring data indicating power availability of access points via which the wireless communication device is capable of communicating along the expected geographical path, determining, from the acquired data indicating power availability, via which of the access points communication is to be established along the expected geographical path for avoiding establishing communication with one or more of the access points indicated to be subjected to power failure, and scheduling the communication to occur via the determined access points.

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 central nodes for developing machine-learning models, the method including receiving, at a central node, at least one episode including a plurality of changes of an environment. The method further includes analysing the episode to extract observations and grouping the observations from among the plurality of observations into a plurality of groups of similar observations. A first machine learning agent is then trained using a first group of similar observations from among the plurality of groups of similar observations, and a second machine learning agent is trained using a second group of similar observations from among the plurality of groups of similar observations, wherein the second group of similar observations is different to the first group of similar observations. The central node obtains a central machine-learning model based on an output from at least one of the trained first machine learning agent and the trained second machine learning agent.

Abstract: A method performed by a content server (100) for determining additional media content to be inserted into a main media content wherein the main media content is in a virtual environment, the method comprising: acquiring (S201) information relating to a user profile of each of a plurality of users that are viewing the main media content; clustering (S202) the plurality of users into one or more user clusters based on the user profile of each of the plurality of users; acquiring (S203) a viewing state of each of the plurality of users that are viewing the main media content, wherein the viewing state of each of the plurality of users comprises biometric data of each of the plurality of users; acquiring (S204) information relating to a network quality; and determining (205) the additional media content for each of the plurality of users by a first reinforcement learning agent based on the one or more user clusters, the viewing state of each of the plurality of users, and the network quality.

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.

Abstract: A user device is configured to receive Hypertext Transfer Protocol, HTTP, requests sent by a user of the device. Information is obtained from the received HTTP requests about areas of interest to the user. The information obtained from the received HTTP requests is combined to generate a machine-generated representation of the areas of interest to the user. Then, data traffic sent to the user is received, and is modified based on the machine-generated representation of the areas of interest to the user.

Abstract: According to an aspect, there is provided a method of operating an analysis node. The method comprises receiving, from a network node in a first communication network to which a first wireless device is attached, behaviour information relating to the behaviour of the first wireless device with respect to the first communication network during a time period following attachment of the first wireless device to the first communication network; analysing the received behaviour information with reference to predetermined behaviour information for wireless devices owned by one or more third parties to determine whether the first wireless device is owned by one of the third parties; and sending an indication of whether the first wireless device is to be authenticated in the first communication network, wherein the indication is based on whether the first wireless device is determined to be owned by one of the third parties.

Abstract: A method performed by network node for determining application of at least one machine learning model from a plurality of machine learning models in a multi-vendor communications network is provided. The network node can receive a request from an actor device operating in a target network to enable running a task for the target network by using a machine learning models from the plurality of machine learning models to perform the task. Responsive to the request, the network node can determine whether a machine learning model from the plurality of machine learning models can perform the task or can be translated to perform the task. Responsive to the determination, the network node can send a communication to the actor device. The communication can include information that a machine learning model is ready to perform the task or that no machine learning model was found to perform the task.