AUTHENTICATION IN PUBLIC LAND MOBILE NETWORKS COMPRISING TENANT SLICES

    公开(公告)号:US20210219256A1

    公开(公告)日:2021-07-15

    申请号:US17055119

    申请日:2018-05-18

    Abstract: Authentication in a public land mobile network, PLMN, having tenant slices is performed by a network element that has: a memory comprising program code; a communication circuitry for communication with entities in the PLMN; and a processing circuitry configured to execute the program code and according to the program code to cause: detecting a registration request from a mobile communication device, MCDt; detecting whether the registration request requests access to a network slice with one-tier authentication with the network slice, and: if yes, causing beginning of authenticating the MCDt with the network slice independently of any authentication between the MCDt and the PLMN.

    MACHINE LEARNING MODEL RENEWAL
    3.
    发明公开

    公开(公告)号:US20240046148A1

    公开(公告)日:2024-02-08

    申请号:US18258728

    申请日:2021-01-29

    CPC classification number: G06N20/00 H04L41/16

    Abstract: There are provided measures for machine learning model renewal. Such measures exemplarily comprise receiving a first machine learning model message including a first machine learning inference model, obtaining network related input data, feeding said first machine learning inference model with said network related input data, receiving, upon unsuitability of said first machine learning inference model for an experienced network condition, a second machine learning model message including a second machine learning inference model, and replacing said first machine learning inference model with said second machine learning inference model.

    MECHANISM FOR REGISTRATION, DISCOVERY AND RETRIEVAL OF DATA IN A COMMUNICATION NETWORK

    公开(公告)号:US20220261383A1

    公开(公告)日:2022-08-18

    申请号:US17592871

    申请日:2022-02-04

    Abstract: An apparatus comprising at least one processing circuitry and at least one memory for storing instructions to be executed by the processing circuitry. The at least one memory and the instructions are configured to, with the at least one processing circuitry, cause the apparatus at least: to receive, from a data consumer, a request for providing data from a data source for processing by the data consumer. The request is processed to obtain, from a configuration management function, a meta data instance related to the requested data, to create a schema of the requested data based on the meta data instance, to obtain a schema for accessing the data being provided by a data source for making the data reusable as history data, and to register the obtained meta data instance and the schema for accessing the data in a repository.

    ML UE CAPABILITY AND INABILITY
    6.
    发明公开

    公开(公告)号:US20230297882A1

    公开(公告)日:2023-09-21

    申请号:US18004570

    申请日:2020-07-07

    CPC classification number: G06N20/00 G06F1/3212

    Abstract: It is provided a method comprising: checking whether a terminal indicates to a network its capability to execute and/or to train a machine learning model; monitoring whether the terminal is in an inability state; informing the network that the terminal is in the inability state if the terminal indicated the capability and the terminal is in the inability state, wherein, in the inability state, the terminal is not able to execute and/or train the machine learning model, or the terminal is not able to execute and/or train the machine learning model at least with a predefined performance.

    RADIO RESOURCE CONTROL PROCEDURES FOR MACHINE LEARNING

    公开(公告)号:US20220279341A1

    公开(公告)日:2022-09-01

    申请号:US17637228

    申请日:2019-09-13

    Abstract: An example method, apparatus, and computer-readable storage medium are provided for radio resource control (RRC) procedures for machine learning (ML). In an example implementation, the method may include receiving, by a user equipment (UE), machine learning (ML) configuration from a network node; collecting, by the user equipment (UE), machine learning (ML) data based at least on the machine learning (ML) configuration received from the network node, the machine learning (ML) data being collected from one or more layers of the user equipment (UE) in a coordinated manner; and transmitting, by the user equipment (UE), the collected machine learning (ML) data to the network node. In another example implementation, the method may include transmitting, by a network node, machine learning (ML) configuration to a user equipment (UE); and receiving, by the network node, machine learning (ML) data from the user equipment (UE), the machine learning (ML) data received in response to the machine learning (ML) configuration transmitted to the user equipment (UE).

    RETRIEVING A CORE NETWORK OR ACCESS NETWORK ASSIGNED USER EQUIPMENT IDENTIFIER

    公开(公告)号:US20220014903A1

    公开(公告)日:2022-01-13

    申请号:US17341798

    申请日:2021-06-08

    Abstract: An apparatus for retrieving at least one of an access network or core network assigned user equipment identifier (NGAP ID) from outside of the access or core network using IP addressing information. The apparatus configured to receive IP addressing information of the user equipment; determine a subscriber permanent user identity for the user equipment from the IP addressing information; generate and transmit a request for the at least one of the access network or core network assigned user equipment identifier, the request including the subscriber permanent identity; and receive the at least one of the access network or core network assigned user equipment identifier.

    RADIO NETWORK CONTROL
    10.
    发明公开

    公开(公告)号:US20240256970A1

    公开(公告)日:2024-08-01

    申请号:US18558996

    申请日:2022-05-03

    CPC classification number: G06N20/00 H04L41/16 H04W24/02

    Abstract: As an aspect, there is provided an apparatus, caused at least to: process, by a central network unit, information on aspects of a machine learning algorithm to be used in an ununiform and/or distributed-manner operating radio communication network, wherein the aspects are characterizing to usage of the machine learning algorithm; examine information on access units in the radio communication network, wherein the information is associated with the location and/or capabilities of the access units; select at least one access unit among the access units to which the machine learning algorithm is to be delivered or from which the machine learning algorithm is to be requested based on the aspects of the machine learning algorithm and on the information on the access units for the machine learning algorithm being used in the ununiform and/or distributed-manner operating radio communication network in a centrally controlled manner.

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