Abstract: Systems, methods, apparatuses, and computer program products for a service-centric mobility-based traffic steering.

Abstract: There are provided measures for network objectives management.

Abstract: According to a method for implementing a training coordination function, at least one training request with which training of a network function instance of an autonomous network of a communication network system is requested is processed. Based on a result of the processing, it is decided whether to approve the at least one training request, or not. If the at least one training request is approved, the training is planned based on the at least one training request, an identification of the at least one training request is returned, and in accordance with the planning, an existing training state associated with the network function instance is stored or updated in a database which is configured to store existing training states associated with network function instances of the autonomous network.

Abstract: An apparatus comprising: at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to: determine (902) that at least one state of a network slice is to be adjusted in a limited geographical area of at least one network resource; and adjust (904, 906, 908) the at least one state of the network slice in the limited geographical area of the at least one network resource.

Abstract: Certain example embodiments provide systems, methods, apparatuses, and computer program products for providing confidence thresholds in an analytics request or subscription. For example, certain embodiments may include a confidence threshold for each reporting threshold (e.g., quality of service (QoS) metric-specific reporting threshold) in an analytics request or subscription (e.g., a network data analytics function (NWDAF) QoS sustainability analytics request or subscription). The confidence threshold (lower bound) can be determined by the consumer application based on their individual needs, e.g., based on the cost or impact of the compensating actions on the predicted QoS sustainability notification. The confidence threshold, in combination with the reporting threshold, may define the conditions for analytics events and/or notifications (e.g., QOS sustainability prediction analytics events and the related notifications).

Abstract: It is provided a method, comprising monitoring if a generic objective for a network is received; translating the generic objective into specific objectives based on a behavioral matrix if the generic objective is received, wherein each of the specific objectives is specific for a respective network element; requesting, for each of the specific objectives, an automation function of the respective network element to achieve the specific objective, identifying, for each of the specific objectives, based on a stored association table, a distributed control function controlling the automation function of the respective network element; informing, for each of the specific objectives, the identified distributed control function on the specific objective for the respective network element; supervising if a feedback is received from one of the distributed control functions, wherein the feedback indicates to which degree one of the specific objectives is achieved; adapting the behavioral matrix based on the feedback.

Abstract: A method comprising: storing a received first and machine learning model instance and a received second machine learning model instance in a cache of a terminal, wherein the first machine learning model instance is associated to a first cell and configured to make, if activated, a first prediction for the terminal, and the second machine learning model instance is associated to a second cell different from the first cell and configured to make, if activated, a second prediction for the terminal; checking if a predefined first requirement is fulfilled; activating the first machine learning model instance to make the first prediction if the predefined first requirement is fulfilled; inferring a decision involving the terminal based on the first prediction if the predefined first requirement is fulfilled; inhibiting to infer the decision involving the terminal based on the second prediction if the predefined first requirement is fulfilled.

Abstract: According to a method for implementing a training coordination function, at least one training request with which training of a network function instance of an autonomous network of a communication network system is requested is processed. Based on a result of the processing, it is decided whether to approve the at least one training request, or not. If the at least one training request is approved, the training is planned based on the at least one training request, an identification of the at least one training request is returned, and in accordance with the planning, an existing training state associated with the network function instance is stored or updated in a database which is configured to store existing training states associated with network function instances of the autonomous network.

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: There are provided measures for evaluation and control of predictive machine learning models in mobile networks. Such measures exemplarily comprise receiving information on a predictive model related to a radio resource management function, obtaining behavior information on an intended behavior of said predicted model, obtaining difference determination information on difference determination with respect to a predictive model prediction and said intended behavior, measuring a network condition, determining a prediction result based on said network condition and said information on said predictive model, determining a behavior result based on said network condition and said behavior information, and evaluating validity of said predictive model based on said prediction result, said behavior result, and said difference determination information.

Abstract: A method for verifying an operation of a mobile radio communication network is provided, wherein the method comprises sending an Initiation Message from a first network entity to a second network entity, the Initiation Message defining an undo request and comprising an UNDO section defining an undo area, a TRIGGERING_CM section indicating changes to the network, and an ASSISTANCE section; receiving an Assistance-Response Message, the Assistance-Response Message comprising the UNDO section, and an ASSISTED_CM section; and sending an Assistance-Acknowledge Message including an ACK flag and the UNDO section from the first network entity to the second network entity.

Abstract: According to an aspect, there is provided a local diagnosis system comprising means for performing the following. The local diagnosis system detects one or more anomaly events associated with a communications network. Each anomaly event defines an anomaly pattern describing a data point in a performance indicator space. Then, the local diagnosis system updates one or more local cluster models to incorporate the one or more anomaly patterns within complexity constraints. Each of the one or more local cluster models corresponds to a different diagnosis label defining a diagnosis. In response to failing according to one or more per-defined criteria to incorporate, in the updating, the one or more anomaly patterns to the one or more local cluster models, the local diagnosis system forwards at least the one or more local cluster models and one or more associated diagnosis labels to a central diagnosis system for further diagnosis.

Abstract: It is provided a method, comprising monitoring if a generic objective for a network is received; translating the generic objective into specific objectives based on a behavioral matrix if the generic objective is received, wherein each of the specific objectives is specific for a respective network element; requesting, for each of the specific objectives, an automation function of the respective network element to achieve the specific objective, identifying, for each of the specific objectives, based on a stored association table, a distributed control function controlling the automation function of the respective network element; informing, for each of the specific objectives, the identified distributed control function on the specific objective for the respective network element; supervising if a feedback is received from one of the distributed control functions, wherein the feedback indicates to which degree one of the specific objectives is achieved; adapting the behavioral matrix based on the feedback.

Abstract: There are provided measures for configuration of power saving groups.

Abstract: Network management apparatus and methods are described. A network management apparatus comprises network data receiving means for receiving network data that is representative of the current condition of a communications network, the network data comprising a plurality of values indicative of the performance of network elements; network data transformation means for transforming the received network data into a network state vector that is indicative of a current state of the network; and network state prediction means for predicting a future network state vector of the network from the current network state vector, the network state prediction means comprising a self-learning prediction module having a memory for storing at least one internal state.

Abstract: Systems, methods, apparatuses, and computer program products for a service-centric mobility-based traffic steering.

Abstract: An apparatus comprising: at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to: receive (600) an indication indicating that positioning accuracy information associated with at least one potential future position of a terminal is to be provided to; and provide (604) the positioning accuracy information associated with the at least one potential future position of the terminal.

Abstract: An apparatus comprising: at least one processor; and at least one memory including computer program code; the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to: determine (902) that at least one state of a network slice is to be adjusted in a limited geographical area of at least one network resource; and adjust (904, 906, 908) the at least one state of the network slice in the limited geographical area of the at least one network resource.

Abstract: Network management apparatus and methods are described. A network management apparatus comprises network data receiving means for receiving network data that is representative of the current condition of a communications network, the network data comprising a plurality of values indicative of the performance of network elements; network data transformation means for transforming the received network data into a network state vector that is indicative of a current state of the network; and network state prediction means for predicting a future network state vector of the network from the current network state vector, the network state prediction means comprising a self-learning prediction module having a memory for storing at least one internal state.

Abstract: Embodiments of the present disclosure are directed to a distributed method and system for independent activation and deactivation of small cells. The method and system consider network traffic at multiple nodes instead of only considering the small cell's own traffic, and may be implemented using existing X2 messages.