Abstract: A method performed by a first network node for a telecommunications network is provided. The method includes receiving a request for a network service. The method further includes dynamically mapping the request for the network service to an internal network slice instance based on unsupervised learning of historical data.

Abstract: A Cognitive Manager (CM) controls a RAN. The CM obtains from the RAN a set of local needs identifying a radio communication performance criterion that is to be satisfied. A set of neighbor needs are obtained from a neighbor CM. An event message is received from the RAN containing a radio metric that does not presently satisfy at least one of the set of local needs. A set of present neighbor improvement action drives being performed or will be performed to satisfy the set of neighbor needs, are obtained from the neighbor CM. A combination of the set of local needs, the set of the neighbor needs, the set of present neighbor improvement action drives, and the radio metric, is used to determine a local improvement action drive that is to be performed by the RAN. An adjustment report identifying the local improvement action drive is provided to the RAN.

Abstract: A computer-implemented method is provided. The method comprises obtaining first correlation values indicating correlations between input features and reward components; obtaining reward weights for the reward components, wherein each of the reward weights indicates a contribution of each of the reward components to a total reward, and applying the reward weights to the first correlation values, thereby generating weighted correlation values which indicate weighted correlations between the input features and the reward components.

Abstract: The invention relates to a method for operating a policy control entity in a cellular network, the method comprising: —determining a quality of service parameter for a data packet session in which one trained model from a plurality of trained models is downloaded to a mobile entity, —determining at least one capacity parameter of the mobile entity, —determining a network transmission parameter of the cellular network, —determining said one trained model from the plurality of different trained models based on a dataset which maps different capacity parameters and transmission capabilities to the plurality of trained models, on the determined capacity parameter, and on the network transmission parameter, —determining routing information indicating where said one trained model is accessible for a transmission to the mobile entity, —transmitting the routing information to a session management entity configured to manage the data packet session in the cellular network.

Abstract: A method performed by a computing device in a communication system for management of delivery of power to at least one radio head from a local battery located proximate to the at least one radio head is provided. The method includes determining a decision about delivery of power from the local battery to the at least one radio head for a future time window. The decision made by a machine learning model based on (i) differentiation of output power data statistics including average power and peak power demands, and (ii) time and location dependent cost data of charging and/or discharging the local battery and power grid utilization. The method further includes outputting the decision about delivery of power from the local battery to the at least one radio head for the future time window.

Abstract: Methods and apparatus for dynamically selecting an action to be taken in an environment. A method comprises receiving, at a ML agent hosting the plurality of ML models, information about a state of the environment, wherein the information about the state of the environment comprises safety information, and analysing the safety information to determine a risk value for the state of the environment. The method further comprises selecting one of the plurality of ML models, wherein the selection is based on the determined risk value, and processing the information about the state of the environment using the selected ML model to generate a state prediction. The method also comprises generating one or more suggested actions to be performed on the environment using the state prediction.

Abstract: The present invention enables accelerated feature vector crafting with a high level of trustworthiness. Heretofore, a system (10) is set up from a data source verification apparatus (12) for verifying the at least one data source (14-1, . . . , 14-n) that generates the new specimen data and a feature vector crafting apparatus (18) for crafting at least one new feature vector from the new specimen data. The feature vector crafting apparatus (18) receiving data after verification of data sources (14-1, . . . , 14-n). The feature vector crafting apparatus (18) automatically generates new feature vectors from new data for forwarding there to an artificial intelligence infrastructure (20).

Abstract: A method performed by a control node for a radio access network. The control node may select an energy source from a plurality of energy sources connected to a base station based on selecting an energy rate of one of the energy sources correlated in time with a radio traffic power usage of the base station. The selection may be made from a plurality of energy rates for the plurality of energy rates correlated in time for each energy source with a radio traffic power usage of the base station. The selected energy rate may improve at least one operating parameter of the selected energy source. The control node may activate the selected energy source for use by the base station. A further method performed by a global control node may be provided.

Abstract: A method is proposed for managing a tree-based learner as following. In an initialisation phase: training the tree-based learner with a first set of specimen data to process input data with an active model chain comprising at least a root model to generate an output, each member of the first set of specimen data having a value of each of a first set of features; in an automated reconfiguration phase: receiving a new set of specimen data; performing a comparison between the new set of specimen data and the first set of specimen data; responding to the executed comparison indicating new values of a feature in the new set of specimen data, by reconfiguring the tree-based learner by the addition of a new sub-model to the active model chain, the new sub-model being trained to process at least the new values of the feature.

Abstract: A technique for configuring a reinforcement learning agent to perform a task using a reward structure derived from a task-specific definition of metric importances is disclosed. A method is performed by a computing unit executing a configurator component and includes obtaining a definition of metric importances specifying, for a plurality of performance-related metrics associated with the task, pairwise importance values each indicating a relative importance of one metric with respect to another metric of the plurality of performance-related metrics for the task, deriving a reward structure from the definition of metric importances, the reward structure defining, for each of the plurality of performance-related metrics, a reward to be attributed to an action taken by the reinforcement learning agent that yields a positive outcome in the respective performance-related metric, and configuring the reinforcement learning agent to employ the derived reward structure when performing the task.

Abstract: A method for managing allocation of network resources at a site in a communication network is disclosed. The method is performed by a resource manager and comprises receiving a notification of an event occurring at a network entity, the network entity being associated with the site and with a network operator. The event comprises at least one of a performance event, a fault event or an update event. The method further comprises determining an implication of the notified event for traffic flow in the network and determining, on the basis of the implication of the notified event for traffic flow in the network, an operational requirement to compensate for the event. The method further comprises obtaining configuration information for fulfilment of the operational requirement and instructing an infrastructure manager for the site to allocate network resources in accordance with the obtained configuration information in order to satisfy the operational requirement.

Abstract: Embodiments herein relate to a method performed by a communication node for handling packets in a communication network, which communication network is an ICN network. The communication node adds a time stamp to a packet, which packet is requesting content of a service or comprises at least a part of the content of the service and is of a format associated with the ICN network, and which time stamp is added into an ICN header of the packet. Furthermore, the communication node transmits the packet with the added time stamp towards another communication node.

Abstract: Decision-making equipment (22) is configured for link change decision-making using reinforcement learning. The decision-making equipment (22) is configured to track rewards (30-1, . . . 30-M) earned for, and outcomes (28-1, . . . 28-M) of, respective link change decisions (26-1, . . . 26-M). In some embodiments, possible outcomes of a link change decision to change a serving link of a wireless device to a target link include at least: a change of the serving link of the wireless device from the target link to another link; and a network-initiated disconnect of the wireless device from the target link. Regardless, the decision-making equipment (22) is also configured to make a link change decision (28-(M+1)) based on the tracked rewards (30-1, . . . 30-M) and outcomes (28-1, . . . 28-M).

Abstract: A computer implemented method for configuring a reinforcement learning agent to perform an efficient reinforcement learning procedure, wherein the reinforcement learning agent comprises a model trained using a machine learning process to determine actions to be performed by the reinforcement learning agent. The method comprises using the model to determine an action to perform, based on values of a set of features obtained in an environment; determining, for a first feature in the set of features, a first indication of a relative contribution of the first feature, compared to other features in the set of features, to the determination of the action by the model; and determining a reward to be given to the reinforcement learning agent in response to performing the action, based on the first feature and the first indication.

Abstract: A method of operating a device that interacts with a physical environment includes determining to take an action on the physical environment wherein the action is based on an output of a computing task, generating a risk level that estimates a risk of physical harm associated with taking the action within the physical environment, obtaining a performance indicator of a communication network between the device and a remote computing device, and determining, based on the risk level and the performance indicator, whether to offload the computing task to the remote computing device or to perform the computing task locally. Related devices are also disclosed.

Abstract: A method performed in a network node for handling an Internet Protocol, IP, packet. The IP packet originates from a source node and addresses to a destination node in an IP communication network. Upon receipt of the IP packet, the network node decapsulates the IP packet to obtain, from a network layer header of the IP packet, an indication of a point in time to drop the IP packet. The point in time to drop the IP packet is based on a first maximum delay of the IP packet between the source node and the destination node. The first maximum delay was obtained from an application software running on the source node. The network node determines whether or not to drop the IP packet based on the indication, using a clock of the network node synchronized with a clock of the source node.

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: A method performed by a first network node in a communication network is provided. The method includes determining, from a machine learning model at the first network node, a value for a network parameter for a cellular network for operation of a communication device based on a set of observations of the communication device, a safety level of the communication device, and a key performance indicator, KPI, of the cellular network. The method further includes signaling the value of the network parameter to a core network node in the cellular network for a resource allocation of the cellular network. A method performed by a core network node in a cellular network is also provided.

Abstract: A computer implemented method of managing power control in a communication system includes generating a graph representation of interdependencies of components of the communication system, wherein the graph representation includes graph nodes corresponding to the components of the communication system and edges between pairs of graph nodes representing dependency relationships between the pairs of nodes. The method generates edge weights for the edges of the graph representation that correspond to the relative importance of the dependency relationship represented by the edge weight, and generates a policy for managing power control by determining an order for switching the components of the communication system on or off based on the edge weights.

Abstract: Embodiments herein disclose e.g. a method performed by a radio network node for controlling power provision of one or more antenna elements associated with a user equipment. The radio network node obtains an indication of a capability relating to an output power of the user equipment; and a state indication of the user equipment indicating an activity level of the user equipment. The radio network node further controls a power arrangement controlling power provision to the one or more antenna elements, based on the state indication and/or the indication of the capability.