METHODS AND DEVICES FOR MULTI-CELL RADIO RESOURCE MANAGEMENT ALGORITHMS

    公开(公告)号:US20250008346A1

    公开(公告)日:2025-01-02

    申请号:US18342807

    申请日:2023-06-28

    Abstract: A device may include a memory configured to store an artificial intelligence or machine learning model (AI/ML) configured to provide an output used in radio resource management of a plurality of cells; and a processor configured to: obtain cell-specific parameters of the plurality of cells of a mobile communication network; select a subset of the plurality of cells based on obtained cell-specific parameters; and cause the AI/ML to be trained with radio access network (RAN)-related data of the subset of the plurality of cells.

    USING AI-BASED MODELS FOR NETWORK ENERGY SAVINGS

    公开(公告)号:US20240298225A1

    公开(公告)日:2024-09-05

    申请号:US18574651

    申请日:2022-10-19

    CPC classification number: H04W36/0072 H04W24/02 H04W52/02

    Abstract: An apparatus for an access node, includes a memory interface to send or receive, to or from a data storage device, measurement information for measurement signaling between a first next generation radio access network (NG-RAN) node and a second NG-RAN node. The apparatus also includes processor circuitry communicatively coupled to the memory interface, the processor circuitry to initiate execution of a machine learning (ML) model by the first NG-RAN node to select an energy saving state for the first NG-RAN node, the second NG-RAN node or a user equipment (UE), and generate one or more messages with an information element (IE) with one or more parameters to request measurement information according to the one or more parameters, the measurement information to comprise feedback information to train the ML model. Other embodiments are described and claimed.

    SYSTEM ENERGY EFFICIENCY IN A WIRELESS NETWORK

    公开(公告)号:US20230188233A1

    公开(公告)日:2023-06-15

    申请号:US17549937

    申请日:2021-12-14

    CPC classification number: H04B17/3912 H04B17/3913 G06N3/02 H04W24/06 H04W48/16

    Abstract: The present disclosure relates to a device for use in a wireless network, the device including: a processor configured to: provide input data to a trained machine learning model, the input data representative of a network environment of the wireless network, wherein the trained machine learning model is configured to provide, based on the input data, output data representative of an expected performance of a plurality of configurations of network components with respect to power consumption and performance of the wireless network; select a configuration of a network component from the plurality of configurations based on the output data of the trained machine learning model; and instruct an operation of the network component according to the selected configuration; and a memory coupled with the processor, the memory storing the input data provided to the trained machine learning model and/or the output data from the trained machine learning model.

    METHODS AND DEVICES TO DETERMINE AN ANTENNA CONFIGURATION FOR AN ANTENNA ARRAY

    公开(公告)号:US20240107443A1

    公开(公告)日:2024-03-28

    申请号:US17953361

    申请日:2022-09-27

    CPC classification number: H04W52/0206 H04B7/0413 H04B7/06 H04B7/08

    Abstract: A device may include a memory and a processor configured to determine, based on first cell data representative of conditions of a cell of a mobile communication network at a first stage associated with a first instance of time, a set of configurations for an antenna array comprising a plurality of antenna elements, wherein each configuration of the set of configurations comprises a configuration in which a subset of the plurality of antenna elements are to be used to perform communications within the cell, and select one or more antenna configurations for the antenna array from the determined set of configurations, wherein each antenna configuration of the one or more antenna configurations are selected based on further cell data representative of the conditions of the cell at a further stage associated with a further instance of time after the first instance of time.

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