COORDINATED CONTROL OF NETWORK AUTOMATION FUNCTIONS

    公开(公告)号:US20230171158A1

    公开(公告)日:2023-06-01

    申请号:US17916673

    申请日:2020-04-03

    CPC classification number: H04L41/0886 H04L41/042 H04L41/0873 H04L43/091

    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.

    PROVIDING CONFIDENCE THRESHOLDS IN AN ANALYTICS REQUEST OR SUBSCRIPTION

    公开(公告)号:US20240214293A1

    公开(公告)日:2024-06-27

    申请号:US18273822

    申请日:2021-02-15

    CPC classification number: H04L43/16 H04L41/5019

    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).

    MAKE-BEFORE-BREAK MOBILITY OF MACHINE LEARNING CONTEXT

    公开(公告)号:US20230422126A1

    公开(公告)日:2023-12-28

    申请号:US18254740

    申请日:2020-11-30

    CPC classification number: H04W36/185 H04W36/322

    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.

    CELL RELATIONS OPTIMIZATION
    5.
    发明申请

    公开(公告)号:US20200351732A1

    公开(公告)日:2020-11-05

    申请号:US16763054

    申请日:2017-11-17

    Abstract: There are provided measures for cell relations optimization. Such measures exemplarily comprise maintaining a table including a plurality of entries, each of said plurality of entries being assigned to a respective one of a plurality of neighboring cells, and each of said plurality of entries comprises overlapping amount information in relation to a source cell and said respective one of said plurality of neighboring cells and overlapping location information in relation to said source cell and said respective one of said plurality of neighboring cells, and utilizing said table for assessment of a suitability of each of said plurality of said neighboring cells for an inter-cell capability.

    CONTEXT-AWARE TRAINING COORDINATION IN COMMUNICATION NETWORK SYSTEMS

    公开(公告)号:US20230412458A1

    公开(公告)日:2023-12-21

    申请号:US18259539

    申请日:2020-12-28

    CPC classification number: H04L41/0895 H04L41/0873 H04L41/16

    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.

    EVALUATION AND CONTROL OF PREDICTIVE MACHINE LEARNING MODELS IN MOBILE NETWORKS

    公开(公告)号:US20230345271A1

    公开(公告)日:2023-10-26

    申请号:US18026710

    申请日:2020-09-18

    CPC classification number: H04W24/04 H04W16/18 H04W24/10

    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.

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