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    EXPLORATION DATA FOR NETWORK OPTIMIZATION
    2.
    发明申请
    EXPLORATION DATA FOR NETWORK OPTIMIZATION 有权

    公开(公告)号：US20220360501A1

    公开(公告)日：2022-11-10

    申请号：US17755137

    申请日：2019-10-23

    Applicant: NOKIA TECHNOLOGIES OY

    Inventor： Teemu Mikael Veijalainen ,  Jani Matti Johannes Moilanen ,  István Zsolt Kovács ,  Wolfgang Zirwas ,  Tero Henttonen

    IPC: H04L41/16 ,  H04L69/22 ,  G06K9/62

    Abstract: An example method, apparatus, and computer-readable storage medium are provided for exploration procedures for network optimization. In one example implementation, the method may include generating, by a first network element, exploration data, the exploration data being generated by the first network element for evaluating performance at a second network element; transmitting, by the first network element, the exploration data to the second network element; and receiving, by the first network element, exploration data feedback from the second network element, the exploration data feedback received from the second network element based on processing of the exploration data by the second network element. In another example implementation, the method may include receiving, by a second network element, exploration data from a first network element; generating, by the second network element, exploration data feedback, the exploration data feedback generated in response to and based on the exploration data received from the first network element; and transmitting, by the second network element, the exploration data feedback to the first network element.

    Exploration data for network optimization
    4.
    发明授权
    Exploration data for network optimization 有权

    公开(公告)号：US12088475B2

    公开(公告)日：2024-09-10

    申请号：US17755137

    申请日：2019-10-23

    Applicant: NOKIA TECHNOLOGIES OY

    Inventor： Teemu Mikael Veijalainen ,  Jani Matti Johannes Moilanen ,  István Zsolt Kovács ,  Wolfgang Zirwas ,  Tero Henttonen

    IPC: H04L69/22 ,  G06F18/21 ,  H04L41/16

    CPC classification number: H04L41/16 ,  G06F18/217 ,  H04L69/22

    Abstract: An example method, apparatus, and computer-readable storage medium are provided for exploration procedures for network optimization. In one example implementation, the method may include generating, by a first network element, exploration data, the exploration data being generated by the first network element for evaluating performance at a second network element; transmitting, by the first network element, the exploration data to the second network element; and receiving, by the first network element, exploration data feedback from the second network element, the exploration data feedback received from the second network element based on processing of the exploration data by the second network element. In another example implementation, the method may include receiving, by a second network element, exploration data from a first network element; generating, by the second network element, exploration data feedback, the exploration data feedback generated in response to and based on the exploration data received from the first network element; and transmitting, by the second network element, the exploration data feedback to the first network element.

    Machine learning in beam selection
    5.
    发明授权
    Machine learning in beam selection 有权

    公开(公告)号：US11870528B2

    公开(公告)日：2024-01-09

    申请号：US17725508

    申请日：2022-04-20

    Applicant: NOKIA TECHNOLOGIES OY

    Inventor： Kalle Petteri Kela ,  Teemu Mikael Veijalainen ,  Hans Thomas Höhne

    IPC: H04B7/06 ,  H04W24/10 ,  H04B17/318

    CPC classification number: H04B7/0639 ,  H04B7/0695 ,  H04B17/318 ,  H04W24/10

    Abstract: Systems, methods, apparatuses, and computer program products for beam selection using data radio bearer specific machine learning are provided. For example, a method can include providing one or more inputs regarding a plurality of beams to a machine learning model. The method can also include obtaining at least one output value regarding the plurality of beams from the machine learning model. The machine learning model can be a data radio bearer specific machine learning model, a data radio bearer group specific machine learning model, or a model trained to output selectively data radio bearer specific values or data radio bearer group specific values.

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