PARTIAL RRC DECODING
    1.
    发明申请

    公开(公告)号:US20250056346A1

    公开(公告)日:2025-02-13

    申请号:US18758521

    申请日:2024-06-28

    Abstract: A user equipment comprising means for: decoding and applying a received cell-change measurement configuration before decoding a received cell-change configuration, wherein the cell-change measurement configuration provides measurement parameters specifying measurements to be performed for managing a handover, and wherein the cell-change configuration provides cell parameters enabling the handover.

    METHODS AND APPARATUSES FOR OBTAINING MEASUREMENTS FOR OFFLINE TRAINING

    公开(公告)号:US20250077891A1

    公开(公告)日:2025-03-06

    申请号:US18817971

    申请日:2024-08-28

    Abstract: Example embodiments provide methods for obtaining measurements for offline training of an artificial intelligence model at a server. A user node is configured to receive, from a server, a first request for one or more additional measurements for generalization during offline training of a two-sided artificial intelligence model; determine one or more additional measurements not available to the user node based on the first request; and transmit, to a network node, a second request for one or more additional measurements when the first request includes one or more measurements not available to the user node. Apparatuses, methods, and computer programs are disclosed.

    TRIGGERING OF ARTIFICIAL INTELLIGENCE/MACHINE LEARNING TRAINING IN NETWORK DATA ANALYTICS FUNCTION

    公开(公告)号:US20250056251A1

    公开(公告)日:2025-02-13

    申请号:US18785480

    申请日:2024-07-26

    Abstract: Triggering of artificial intelligence/machine learning training in a network data analytics function is provided. A method for triggering artificial intelligence/machine learning training in a network data analytics function may include obtaining at least one machine learning model for training or retraining based on measurement data of a network. The method may also include determining that data collection is required prior to training or retraining the at least one machine learning model, and receiving one or more measurement reports that includes at least one dataset from the data collection. The method may further include determining whether additional assisted information from one or more network devices is required for training or retraining. The at least one machine learning model may be trained or retrained based on all collected datasets, which includes the at least one dataset from the data collection.

    ASSISTANCE FOR FUNCTIONALITY SELECTION AT A NETWORK

    公开(公告)号:US20250056394A1

    公开(公告)日:2025-02-13

    申请号:US18774549

    申请日:2024-07-16

    Abstract: Methods, apparatuses, and computer program products provide means for functionality selection assistance to determine selection of a preferred functionality for an Artificial Intelligence/Machine Learning model feature. An example method includes: receiving a request for supported functionalities from a network node; identifying the supported functionalities; determining functionality selection assistance information; and providing an indication of the supported functionalities and the functionality selection assistance information to the network node. A method can optionally include receiving an indication of a selected functionality of the supported functionalities based, at least in part, on the functionality selection assistance information; and employing the selected functionality to support a feature.

    CONDITIONAL CELL CHANGE
    5.
    发明申请

    公开(公告)号:US20250056335A1

    公开(公告)日:2025-02-13

    申请号:US18798105

    申请日:2024-08-08

    Abstract: Example embodiments of the present disclosure relate to devices, methods, apparatuses and computer readable storage medium for conditional cell changes. In a method, a network device transmits, to a plurality of candidate target network devices, an indication of a plurality of candidate target cells of the plurality of candidate target network device. The network device receives, from the plurality of candidate target network devices, first information related to measurements for changes between the plurality of candidate target cells. Then the network device transmits, to a terminal device, second information related to the measurements for use in executing of the change from a first candidate target cell to a second candidate target cell among the plurality of candidate target cells after handover from a source cell to the first candidate target cell.

    METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCT FOR COLLECTING DATA FROM USER EQUIPMENT DEVICES

    公开(公告)号:US20250056289A1

    公开(公告)日:2025-02-13

    申请号:US18790220

    申请日:2024-07-31

    Abstract: Method, apparatuses, and computer program products provide means for establishing a mechanism through which a network can configure user equipment to determine how to process an indication of region of the user equipment location when establishing a network connection. An example method includes: receiving, from a visited network, a first indication of a region of an apparatus location, where the apparatus includes a User Equipment (UE); and determining, based on a second indication in the apparatus, whether the apparatus shall ignore the first indication for network selection, where the second indication is received from a home network of the apparatus via a container transparent to the visited network. The region of the apparatus location may include a country of the apparatus location. The container may include a Steering of Roaming transparent container. The container may be delivered via a rejection message.

    MODEL ID-BASED AI/ML MODEL UPDATE MANAGEMENT FRAMEWORK AND ITS USE

    公开(公告)号:US20250053872A1

    公开(公告)日:2025-02-13

    申请号:US18797512

    申请日:2024-08-08

    Abstract: A network element evaluates performance of a first AI/ML model being used by a UE. The network element sends, based on the evaluation, configuration to a network entity involved in performing model retuning of the first AI/ML model to aid in the model retuning. The network element monitors and evaluates performance of a second AI/ML model that is a retuned version of the first AI/ML model. The first and second AI/ML models are from a same lineage of AI/ML models. The network element stores, in response to the evaluation of the second AI/ML model, the second AI/ML model for use by other UE(s). A UE receives the configuration, and performs operation(s) to aid in the performing retuning. The retuning creates a second AI/ML model that is a retuned version of the first AI/ML model. The UE switches from the first AI/ML model to the second AI/ML model.

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