Abstract: Embodiments of the present disclosure provide a method for controlling an operation of a wearable device that is connected to a network node in a wireless communication network. The method is performed by the wearable device. The method includes identifying an activity status for user activity using the wearable device and providing information including at least one parameter associated with the wearable device and the identified activity status. The method includes transmitting the provided information to the network node in the wireless communication network and receiving, from the network node, control information including at least one indication for controlling the operation of the wearable device. The method further includes controlling the operation of the wearable device in accordance with the control information received from the network node. Corresponding wearable device, network node and computer program products are also disclosed.

Abstract: A method performed by a network node for handling operation of a User Equipment (UE) in a wireless communications network. The method includes obtaining a first value of a quality of service (QoS) characteristic for a service that is associated with a task performed by the UE, obtaining a set of second values of the QoS characteristic for the service, using the obtained set of second values and the obtained first value in a machine learning, e.g. reinforcement learning, model to determine a value of an operating parameter of the UE for performance of the task by the UE, and transmitting an indication of the determined value of the operating parameter for controlling operation of the UE in the wireless communications network based on the determined value of the operating parameter.

Abstract: Embodiments of the present disclosure provide a method for handling adjacent channel interference (ACI) from a first user equipment (UE), in an industrial environment including a plurality of industrial devices. The first UE being connected to a first network node located outside the industrial environment and each industrial device being equipped with a second UE connected to a second network node, located within the industrial environment. The method is performed by the second network node. The method includes detecting presence of the first UE within the industrial environment. The method includes determining that the first UE is causing ACI to one or more second UEs within the industrial environment. The method includes transmitting control information comprising including an indication to the one or more second UEs for causing the one or more second UEs to handle the determined ACI.

Abstract: Embodiments of the present disclosure provide a method performed by a wireless device for handling communication with a network node. The wireless device is connected to an industrial device in an industrial environment and the wireless device is in communication with the network node using a communication circuitry. The communication circuitry includes a plurality of components interconnected for communicating with the network node. The method includes detecting occurrence of a failure event in the communication circuitry and identifying at least one component of the plurality of components causing the failure event. The method further includes determining at least one component from the plurality of components, to handle the communication with the network node and enabling the at least one component for the communication with the network node.

Abstract: A method is provided that is performed by a first node of a fat tree network for management of a plurality of traffic flows having differentiated service requirements. The method includes receiving, from a simulation model or a testbed environment, joint observations for the traffic flows. The traffic flows correspond to routing paths comprising different combinations of at least one leaf node, at least one spine node, and at least one super spine node of the fat tree network per routing path. The method further includes identifying, with a first reinforcement learning model, a first action to take to reduce or prevent congestion at the first node. The first action includes a reconfiguration of the first node for an identified routing path. The method further includes outputting, to a controller node, the reconfiguration of the first node.

Abstract: A computer-implemented method performed by a first node (101). The method is for handling managing one or more indications. The first node (101) operates in a communications system (100). The first node (101) determines (304) a scheduling of a transfer of data to or from a device (130) along a predicted route (140). The predicted route (140) is to be followed by the device (130) during a time period. The determining (304) of the scheduling is further based on a type of energy used by a plurality of cells (121, 122) providing radio coverage along the predicted route (140). The first node (101) initiates (305) providing one or more indications to the device (130) based on a first result of the determining (304) of the scheduling. The device (130) receives (402) the one or more indications, determines (403) whether or not to perform the data transfer based on them, and initiates (404) performing the data transfer accordingly.

Abstract: A method performed by a network node in a communication network for responding to a query of a first communication device to a second communication device related to a feature of a sense is provided. The method includes receiving a plurality of signatures generated from data including measurements of senses from at least one sensor. Each signature includes a plurality of features of the senses extracted from the measurements and mapped to each signature. The method further includes clustering a signature into a cluster having a classification. The method further includes searching a database that includes a mapping of features to defined labels of senses for the clusters to extract a response to the query. The method further includes signalling the response to the query of the first communication device.

Abstract: Embodiments described herein relate to methods and apparatuses for generating one or more answers relating to a machine learning, ML, model. A method in a first node comprises obtaining one or more queries relating to a first output of the ML model, wherein the first output of the machine learning, ML, model is intended to fulfil one or more requirements in an environment; for each of the one or more queries performing a reinforcement learning process.

Abstract: A method performed by a network node for handling one or more operations in a communications network comprising a plurality of computing devices performing one or more tasks. The network node obtains initial parameters relating to the plurality of computing devices, environment and the communications network; and generates a plan by taking one or more operation goals involving the plurality of computing devices into account as well as the obtained initial parameters, wherein the generated plan relates to operation of the plurality of computing devices. The network node further computes a number of back-up plans, wherein the number of back-up plans are taking one or more events into account wherein the one or more events relate to operation of the plurality of computing devices; and executes one or more operations using the generated plan, and in case the one or more events occur, using a computed back-up plan.

Abstract: A quality assurance method, performed in a device, includes defining a set of states of a joint system of a communication service provider and a consumer of the service, defining a set of actions each corresponding to a change of one or more QoS characteristics of the service, using a partially observable Markov decision process (POMDP) to define a control policy mapping beliefs about the state of the joint system to preferred actions taken on the service, receiving observations indicative of performance indicators of a process performed by the consumer, updating a belief about the state of the joint system based on the observations, and using the control policy to control the service to make the service operate more efficiently while fulfilling a service level agreement (SLA) with the consumer. A device, joint system, computer program and computer program product are also provided.

Abstract: A computer implemented method is provided for training a policy for managing an environment in a communication network. The method includes selecting an action which will maximise a future predicted value of a reward function, given a current belief state of the environment, and recording an explanation tree for the selected action. The explanation tree includes a representation of the current belief state, available actions, and predicted reward function values associated with the available actions. The method further includes causing the selected action to be executed and obtaining an observation following execution of the selected action. The method further includes using a second policy function to generate an updated current belief state based on the obtained observation, and on a probability that an explanation for the selected action would be accepted by an entity querying the selected action, the explanation being generated using the explanation tree.

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 computer implemented method is disclosed for generating a policy for managing a configuration of internal components of a communication network node. The method includes obtaining performance data and generating a model of the communication network node The method further includes using the model to generate a first data set; and extracting, from the first data set: a set of conditional probabilities of operational state transition for the communication network node; and a set of conditional probabilities of changes in observed measure of performance for the communication network node. The method further comprises includes combining the extracted sets of conditional probabilities with a reward function to form a configuration model and generating a solution to the configuration model including a policy that is operable to propose a change in configuration of an internal component of the communication network node based on an observed measure of performance of the communication network node.

Abstract: A computer implemented method for determining, in coordination with a second device, whether a first device can perform a first action. The method comprises sending (202) a first message to the second device, wherein the first message comprises i) an indication of the first action to be performed by the first device and ii) a first time interval indicating when the first action is to be performed. the method further comprises receiving (204) a second message from the second device according to an argumentation protocol defined between the first device and the second device, wherein the second message comprises i) a first response related to whether the first device can perform the first action, and ii) a second time interval in which the first response is applicable. The first response is valid according to the argumentation protocol if the second time interval overlaps the first time interval.

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: According to one or more embodiments, a node for temporal plan verification and timed game automata, TIGA, controller strategy synthesis for coordination of a plurality of agents is provided. The node includes processing circuitry that is configured to determine a first model of a plurality of temporal plans for timed operations of the plurality of agents violates at least one predefined safety rule, generate the TIGA controller strategy for controlling the timed operations of at least a subset of the plurality of agents where the TIGA controller strategy meets the at least one predefined safety rule and being based on the first model, and cause transmission of the TIGA controller strategy to a dispatch device for translating the TIGA controller strategy to instructions for the plurality of agents.

Abstract: Methods (600) and systems (700) for selecting a frequency band for a device. In one aspect, the method comprises receiving (s602) from the device a request for a particular service type. The method further comprises determining (s604) a set of two or more frequency bands that are available for the device. Said set of two or more frequency bands comprises a first frequency band and a second frequency band. The method further comprises selecting (s606), based on the particular service type being requested and at least one of a knowledge base or a machine learning (ML) model, one of the frequency bands included in the set of two or more frequency bands.

Abstract: It is provided a method for adjusting a network of sensor devices. The network also comprises an aggregation node for aggregating sensor data from at least two of the sensor devices to processed data and an application node configured to approach an application goal based on the processed data. The method is performed in a network device. The method comprises repeatedly performing the steps of: receiving an evaluation of performance of the application node in relation to the application goal; determining that the evaluation indicates that an adjustment is needed; responsive to determining that an adjustment is needed, determining an operation adjustment of at least one of the aggregation node and the sensor devices; and triggering the adjustment.

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: According to one or more embodiments, a node is provided where the node is configured to: transform a plurality of service level agreement, SLA, values into a plurality of uniform language values associated with at least a first quality of service, QoS, composite metric, and optionally generate a first end-to-end composite SLA based at least on a first set of the plurality of uniform language values.