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: A method performed by a control node (800) for determining whether two wireless devices meet a location criterion for closeness, the method comprising: obtaining (701), from the two wireless devices, measurements of a reference signal transmitted in a beam by a radio network node to a plurality of measurement points that are spatially distributed around the two wireless devices; and determining (702) whether the two wireless devices meet the location criterion based on the obtained measurements.

Abstract: A method is provided. The method includes generating a first data by using a first decoder model with a first set of target features, wherein the first decoder model is based on the first source domain. The method includes updating a final set of target features and final data based on the generated first data. The method includes generating a second data by using a second decoder model with a second set of target features, wherein the second data that is generated is conditioned on the first set of target features and wherein the second decoder model is based on the second source domain. The method includes updating the final set of target features and final data based on the generated second data. The method includes training a target-domain model using the final data and the final set of target features.

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: A computer implemented method performed by a node in a communications network for matching parameters in a target network configuration with corresponding key performance indicators, KPIs, in the communications network. The method comprises obtaining (202) a first set of connections between a first subset of the parameters and a first subset of the KPIs. The method further comprises representing (204) the parameters as a first knowledge graph, representing (206) the KPIs as a second knowledge graph, and using (208) a graph-based machine learning process on the first knowledge graph and the second knowledge graph to match a second parameter to a second KPI.

Abstract: A method, in a first network function, for providing one or more platoon recommendations for platooning of a plurality of communication devices the method comprising: obtaining mobility information for the plurality of communication devices; determining, based on the mobility information, a platoon recommendation comprising a subset of the plurality of communication devices to be grouped into a platoon and a head communication device of the subset of communication devices to be responsible for reporting reference signal measurements on behalf of the platoon; and transmitting the platoon recommendation to a second network function.

Abstract: The present disclosure relates to a method of a wireless communication device (103) of facilitating control of antenna tilt for a radio base station (101), a method of a radio base station (101) of facilitating control of antenna tilt at the radio base station (101) and further a wireless communication device (103) and a radio base station (101) performing the respective method. In a first aspect a method of a wireless communication device (103) of facilitating control of antenna tilt for a radio base station (101) is provided.

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: Methods (500) and devices (800) for determining a priority level for a first application or a first application type. The method comprises receiving (s502) a first usage data reporting message, the first usage data reporting message comprising a first masked usage value generated by a first UE (102) using i) a first usage value associated with a first application or a first application type and ii) a mask value. The method further comprises receiving (s504) a second usage data reporting message, the second usage data reporting message comprising a second masked usage value generated by a second UE (104) using i) a second usage value associated with the first application or the first application type and ii) the mask value. The method further comprises combining (s506) the first masked usage value and the second masked usage value, thereby generating a combined usage value; and using (s508) the combined usage value to determine a priority level for the first application or the first application type.

Abstract: Embodiments herein may relate to a method performed by a radio network node for handling communication over a bandwidth interval for one or more UEs, in a wireless communication network. The radio network node determines to change a currently used bandwidth interval based on a current state of usage of resources in the 5 currently used bandwidth interval related to number of UEs served and/or a current state of positions of the number of UEs served. With the proviso that it is determined to change the currently used bandwidth interval, the radio network node calculates a changed bandwidth interval to use. The radio network node then adjusts the currently used bandwidth interval to the calculated bandwidth interval; and uses the adjusted 10 bandwidth interval for communicating with the one or more UEs.

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: There is provided a method for handling training of a machine learning model. The method is performed by a coordinating entity that is operable to coordinate the training of the machine learning model at one or more network nodes. In response to receiving a request to train the machine learning model, a first network node is selected (402), from a plurality of network nodes, to train the machine learning model based on information indicative of a performance of each of the plurality of network nodes and/or information indicative of a quality of a network connection to each of the plurality of network nodes. Transmission of the machine learning model is initiated (404) towards the first network node for the first network node to train the machine learning model.

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: A method performed by a first node in a distributed network is provided. The method includes receiving a request from a second node for transfer learning of a machine learning model for a use case. The request includes a description of a local environment and a use case identifier at the second node. The method further includes identifying a matching criteria for the local environment of the second node based on the use case. The method further includes determining whether at least one distributed node from a plurality of distributed nodes in the distributed network satisfies at least one of a match or a closest match to the matching criteria.

Abstract: A method of operating a radio network node to adjust power consumption of a telecommunications network is provided. The method includes determining a traffic prediction representing how each node of a set of nodes will interact with the radio network node over a period of time using a combined traffic model based on a traffic model of each node in the set of nodes. The method further includes determining to enable or disable at least one power related feature of the radio network node based on the traffic prediction.

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.