Mechanism for integrating non-standard related data sources into communication network

    公开(公告)号:US12250107B2

    公开(公告)日:2025-03-11

    申请号:US18250239

    申请日:2020-12-01

    Abstract: An apparatus for use by a communication network element or function configured to act as a management controller in a communication network, the apparatus comprising at least one processing circuitry, and at least one memory for storing instructions to be executed by the processing circuitry, wherein the at least one memory and the instructions are configured to, with the at least one processing circuitry, cause the apparatus at least: to conduct a creation and activation of at least one data source function instance for configuring a data source being not standardized for usage in the communication network to provide data to a data consumer formed by a communication network element or function in a data format allowing the data consumer to process the data, wherein the creation and activation comprises associating the data source function instance to meta data describing either a non-communication network standardized data type or a proprietary data type using attributes defined to the respective data type, and associating the data source function instance to context data describing a generation time of data to be provided to the data consumer and a scope of data to be provided to the data consumer including a relation to a part of the communication network.

    METHOD AND APPARATUS FOR NETWORK DIGITAL TWIN-BASED FAULT INJECTION ANALYSIS

    公开(公告)号:US20250088524A1

    公开(公告)日:2025-03-13

    申请号:US18817426

    申请日:2024-08-28

    Abstract: A network node is configured to support detection and/or diagnosis of network anomalies in a target communication network based on a Network Digital Twin, NDT, simulating at least a part of the target communication network, and comprises at least one processor; and at least one memory storing instructions that, cause the first network node at least to: provide, to a second network node, at least one anomaly injection case generated based on a simulation configuration and/or configure at least one anomaly injection case in the NDT, the NDT being provided at the second network node; receive, from the second network node, a simulation signature and/or anomaly diagnosis of a simulation of the anomaly injection case on the NDT; and deploy diagnosis knowledge, generated based on the received simulation signature and/or anomaly diagnosis, in a Network Anomaly Detection Function, NADF, for the target communication network and/or provide the anomaly diagnosis.

    METHOD AND APPARATUS FOR FEDERATED TRAINING
    3.
    发明公开

    公开(公告)号:US20240249203A1

    公开(公告)日:2024-07-25

    申请号:US18408775

    申请日:2024-01-10

    CPC classification number: G06N20/20 G06N3/0455 H04L9/008

    Abstract: An apparatus for federated training, the apparatus comprising means for:



    Transmitting a first implementation (22) of a data-processing model to a first distributed trainer, wherein the first implementation of the data-processing model comprises a first hidden part (221) and a first open part (222),
    Transmitting a second implementation (23) of the data-processing model to a second distributed trainer, wherein the second implementation of the data-processing model comprises a second hidden part (231) and a second open part (232),
    Receiving a first training gradient from the first distributed trainer and a second training gradient from the second distributed trainer, wherein the first gradient relates to the first open part of the first implementation of the data-processing model, wherein the second gradient relates to the second open part of the second implementation of the data-processing model,
    Updating the data-processing model using the first gradient and the second gradient.

    INFERENCE-AWARE ML MODEL PROVISIONING

    公开(公告)号:US20230060071A1

    公开(公告)日:2023-02-23

    申请号:US17891866

    申请日:2022-08-19

    Abstract: There are provided measures for enabling/realizing inference-aware ML (machine learning) model provisioning, e.g. to support network data analytics, in a mobile/wireless communication system. Such measures exemplarily comprise that ML model request information, including model-related information indicating one or more properties of a requested ML model and inference-related information indicating one or more properties of execution of inference based on the requested ML model, is provided from a first network entity (representing a service consumer of a network data analytics service) to a second network entity (representing a service provider of the network data analytics service), the second network entity specifies an ML model to be provisioned based on the ML model request information, and ML model information about the specified ML model is provided from the second network entity to the first network entity.

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