Abstract: Method comprising: receiving an update information on an updated value of a parameter of a network function and an identifier of a managing function responsible for the updating of the value of the parameter; defining a favorable range of values of the parameter based on the update information and a history of previous values of the parameter, wherein the history comprises, for each of the previous values of the parameter, the identifier of a respective managing function responsible for updating the value of the parameter to the respective previous value; calculating a new value of the parameter by optimizing a utility function and taking the favorable range as a constraint for the new value; updating the value of the parameter of the network function to the new value.

Abstract: It is provided a method comprising: receiving one or more received data sets, wherein each of the received data sets comprises data each representing a value of a respective status parameter of a system, and at least one of the data sets comprises a respective value quality score representing a quality of the value of the respective status parameter: calculating, by a cognitive function, an optimal configuration range set and a confidence score of the optimal configuration range set based on the one or more received data sets; providing the calculated optimal configuration range set and the confidence score to a controller.

Abstract: It is provided a method comprising: checking if at least one of one or more cognitive functions is suspected for manipulative behavior; providing an indication that the at least one of the one or more cognitive function is suspected for manipulative behavior if the at least one cognitive function is suspected for manipulative behavior.

Abstract: The disclosure relates to an apparatus configured to: receive (900), from user equipment, user equipment capability information; compute (902) statistics for a set of user equipment capability classes based on the user equipment capability information; and cause (904) a network management function to train models specific for at least a subset of user equipment capability classes selected amongst the set of user equipment capability classes based on the statistics for a set of user equipment capability or to train a model generic for at least a subset of user equipment capability classes selected amongst the set of user equipment capability classes based on the statistics for a set of user equipment capability classes.

Abstract: A computer-implemented data structure for a service instance object and related methods and network node devices are disclosed. The computer-implemented data structure for a service instance object of a network and service management system comprises a first data section configured to store general service instance data, a second data section configured to store end-to-end (E2E) service data, a third data section configured to store E2E service process flow specific data, and a fourth data section configured to store management domain specific data.

Abstract: An apparatus comprising means configured to: receive a set of service offer descriptors, the service offer descriptor set defining one or more attributes, at least one of the attributes being associated with a plurality of different options, the set of service offer descriptors being associated with a service provider; receive a set of service requirements for a service to be provided, said set of service requirements defining one or more attributes for the service and for one or more of the attributes an associated option; and provide a set of service order descriptors for the service to be provided, said set of service order descriptors based on said set of service offer descriptors and comprising for at least one attribute of the set of service offer descriptors information indicating a selected option of the plurality of different options, in dependence on the service to be provided.

Abstract: Fulfillment of an intent which has been submitted to an intent-driven network management system is tracked. When information about fulfillment of the intent is received, a feedback report including a measurement that indicates a level of satisfaction with the fulfillment of the intent is provided.

Abstract: A management service producer or data repository of a communication network provides at least one of the following: information about knowledge available for sharing, or knowledge available for sharing, and executes a transfer learning process for sharing at least part of the knowledge. A management service consumer of the communication network generates at least one of the following: a request for information on available knowledge from the management service producer or data repository, a request for available knowledge from the management service producer or data repository, or a request for executing a transfer learning process between the management service producer or data repository and an ML entity or ML-enabled function of the communication network, and manages the request for available knowledge or the request for executing the transfer learning process, and/or adapts content of the request for available knowledge or the request for executing the transfer learning process.

Abstract: The disclosure relates to an apparatus comprising means for: receiving, at a first closed loop function from a second closed loop function, one or more proposed actions to be taken by the second closed loop function with respect to a managed entity, the first and second closed loop functions operating on the managed entity; determining, at the first closed loop function, one or more further actions to be taken, wherein the determining is based on the at least the one or more proposed actions to be taken by the second closed loop function; and causing, by the first closed loop function, the determined one or more further actions to be taken.

Abstract: Various example embodiments relate to cooperative analytics across multiple network management domains. An apparatus may request management domain analytics from a plurality of management domains of a communication network. The apparatus may receive management domain analytics results from the plurality of management domains and cause reconfiguration of the communication network based on the management domain analytics results. Apparatuses, methods, and computer programs are disclosed.

Abstract: An apparatus for an access point configured to provide a first coverage area, the apparatus comprising: means for determining to deactivate or activate at least part of a second coverage area located within the first coverage area, the second coverage area being provided by a booster cell; means for identifying said booster cell: and means for transmitting a request to said booster cell to deactivate and/or activate the at least part of the second coverage area in dependence on said determination, wherein said request identifies whether the activation and/or deactivation is to be provided at a beam-level or at a cell-level of the second coverage area.

Abstract: The present disclosure provides a method comprising monitoring, by an update service producer receives, for a request to update an artificial intelligence/machine learning capability influencing a network service, and instructing, by the update service producer, a first support function to update the artificial intelligence/machine learning capability to an update version when the request is received.

Abstract: There is provided an apparatus comprising: at least one processor and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to receive, at an analytics producer, a request for analytics, from an analytics consumer, wherein the request comprises a first time period, TA, determine a second time period, DS for an expected time to provide the requested analytics at the analytics producer, provide the analytics based on the request in a third time period, TD, determine, based on TA, DS and TD, which of at least one of a plurality of parameters is met and increment a counter for the determined parameter.

Abstract: Systems, methods, apparatuses, and computer program products for cluster based local ML model training host selection in asynchronous federated learning model collection. A method may include transmitting local model parameters following training of a model of at least one network element to a network node to be used to cluster the at least one network element with one or more other network elements. The method may also include training, by one or more network elements selected from the cluster, an aggregated model using the local model parameters. The method may further include transmitting, by the one or more network elements selected from the cluster, updated local model parameters of the at least one network element as a result of the training to the network node.

Abstract: Apparatuses and methods in a communication system are disclosed. In a network element, an encoder module obtains as an input network data that is representative of the current condition of the communications network, the network data comprising a plurality of values indicative of the performance of network elements and performs (800) feature reduction providing at its output a set of activations. A clustering module performs (802) batch normalisation and an amplitude limitation to the output of the encoder module to obtain normalised activations. A clustering control module calculates a projection of the normalised activations and determines (804) a clustering loss. A decoder module calculates (806) a reconstruction loss. The network element backpropagates the reconstruction loss and the clustering loss through the modules.

Abstract: First user equipments are detected out of a plurality of user equipments of a cellular communication system (S201). The user equipments respectively correspond to a distributed node of a federated machine-learning concept and respectively generate a partial machine-learning model, wherein partial machine-learning models generated by the plurality of user equipments are to be used to update a global machine-learning model at the network side of the cellular communication system. The first user equipments are user equipments comprising ready partial machine-learning models.

Abstract: Systems, methods, apparatuses, and computer program products for supporting cognitive service driven handover are provided. One method may include signaling at least one user equipment to start performing and logging quality of experience (QoE) related measurements, and transmitting a request for a report on handover performance to the at least one user equipment. The method may also include receiving the handover performance report, from the at least one user equipment, including at least one service identifier of a service running on the at least one user equipment and the quality of experience (QoE) related measurements.

Abstract: There is provided an apparatus arranged to: receive, from a configuration controller, a request for feedback on a network configuration in a communications network; request feedback on the network configuration from a plurality of network critic functions; aggregate feedback received from at least part of the plurality of network critic functions in response to the request; and provide the aggregated feedback to the configuration controller.

Abstract: According to an example aspect of the present invention, there is provided an apparatus comprising at least one processing core, at least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processing core, cause the apparatus at least to generate, using a machine learning mechanism, a set of test instructions, wherein each one of the test instructions is configured to trigger a first network to change one or more technical characteristics of the first network, the test instructions not comprising configuration changes in nodes of the first network that are usable in changing the one or more technical characteristics of the first network, and use a block of text as input in the generation of the set of test instructions.

Abstract: Systems, methods, apparatuses, and computer program products for managing machine learning training. One method may include a machine learning training function receiving a request to instantiate a machine learning training job from a consumer, and instantiating the requested machine learning training job. The machine learning training function may transmit a notification to the consumer indicating that the machine learning training job has been instantiated.