AI/ML based mobility related prediction for handover

    公开(公告)号:US12238602B2

    公开(公告)日:2025-02-25

    申请号:US17808072

    申请日:2022-06-21

    Abstract: A source network node and a UE may obtain at least one mobility related prediction associated with the UE or at least one target network node, the at least one mobility related prediction being derived by at least one neural network, and the source network node may handover the UE from the source network node to the at least one target network node based on the at least one mobility related prediction. The target network node may receive the handover request, obtain at least one mobility related prediction associated with the UE or the target network node, and output for transmission a handover request ACK, the handover request ACK based at least in part on the at least one mobility related prediction.

    Common frequency resources for different numerologies

    公开(公告)号:US12177051B2

    公开(公告)日:2024-12-24

    申请号:US17449504

    申请日:2021-09-30

    Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive an indication of a first common frequency resource (CFR) within a bandwidth part (BWP) and a second CFR within the BWP. Accordingly, the UE may receive, using a first numerology, a non-group-common communication at least partially in the BWP. The UE may also receive, using the first numerology, a group-common communication at least partially in the first CFR, and receive, using a second numerology, a group-common communication at least partially in the second CFR. As an alternative, the UE may receive an indication of a CFR within a BWP. Accordingly, the UE may receive, using a first numerology, a non-group-common communication in the BWP and a first group-common communication in the CFR, and receive, using a second numerology, a second group-common communication in the CFR. Numerous other aspects are described.

    Inter-system and event-triggered mobility load balancing

    公开(公告)号:US11968563B2

    公开(公告)日:2024-04-23

    申请号:US17371998

    申请日:2021-07-09

    CPC classification number: H04W28/0865 H04W24/10 H04W28/0284 H04W28/0958

    Abstract: Methods, systems, and devices for wireless communications are described. Some wireless communications system may utilize an inter-system information report message (e.g., a self-organizing network (SON) information report message) to support inter-system mobility load balancing (MLB). For example, a first node, operating in accordance with a first radio access technology (RAT), may receive an information report message from a second node operating in accordance with a second RAT. The information report message may include a periodic load reporting request information element (IE) or an event-triggered load reporting request IE. In response, the first node may determine a traffic load based on the load reporting request and transmit, to the second node, an information report message which includes one or more IEs for reporting the determined traffic load. The exchange of the load information via the IEs may enable for MLB between nodes of different RAT.

    Selective measurement reporting for a user equipment

    公开(公告)号:US11937114B2

    公开(公告)日:2024-03-19

    申请号:US17444437

    申请日:2021-08-04

    CPC classification number: H04W24/10 H04W24/08 H04W76/10

    Abstract: A method of wireless communication includes determining, based on a plurality of network measurements performed by a user equipment (UE), one or more measurement log files associated with the plurality of network measurements. The method further includes receiving, by the UE from a network device, a request associated with the one or more measurement log files. The request indicates at least one measurement filter. The method further includes transmitting, by the UE to the network device, a response to the request. The response includes first measurement results of the one or more measurement log files selected based on the at least one measurement filter and excludes second measurement results of the one or more measurement log files based on the at least one measurement filter.

    SYSTEMS AND METHODS OF PARAMETER SET CONFIGURATION AND DOWNLOAD

    公开(公告)号:US20240064065A1

    公开(公告)日:2024-02-22

    申请号:US17891974

    申请日:2022-08-19

    CPC classification number: H04L41/16 H04W76/11

    Abstract: Example implementations include a method, apparatus and computer-readable medium of wireless communication by a user equipment (UE), comprising receiving parameter set configuration information from a network entity, the parameter set configuration information corresponding to a model structure employed in a machine learning operation by the UE for the wireless communication. The implementations further include activating a parameter set in response to an activation condition, the parameter set identified within the parameter set configuration information as being associated with the activation condition.

    CONDITIONAL ARTIFICIAL INTELLIGENCE, MACHINE LEARNING MODEL, AND PARAMETER SET CONFIGURATIONS

    公开(公告)号:US20230412470A1

    公开(公告)日:2023-12-21

    申请号:US17841339

    申请日:2022-06-15

    CPC classification number: H04L41/16 G06N20/20 H04W88/02

    Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a message from a network entity indicating a set of machine learning models, a set of parameter sets, or both and one or more usage conditions associated with the machine learning models and parameter sets. Based on a usage condition being satisfied, the UE may select a machine learning model, a parameter set, or both for generating a machine learning inference. For example, the UE may select the machine learning model or the parameter set based on a priority, whether sufficient input data is provided, or based on other usage conditions. The UE may generate the machine learning inference using the selected machine learning model or the selected parameter set, and the UE may transmit a report indicating an output of the machine learning inference to the network entity.

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