RADIO RESOURCE MANAGEMENT
    1.
    发明公开

    公开(公告)号:US20240334396A1

    公开(公告)日:2024-10-03

    申请号:US18598082

    申请日:2024-03-07

    CPC classification number: H04W72/04

    Abstract: According to an example aspect of the present invention, there is provided an apparatus configured to obtain, from user equipment-level operating statistics from a radio access network, network slice-level operating statistics concerning plural network slices in the radio access network, update, using a plurality of processes, each process specific to a distinct network slice, network slice specific cost indices based at least in part on the network slice-level operating statistics, each cost index indicating a relative resource cost of increasing a radio resource allocation of a respective network slice, each process running a distinct neural network to update the respective cost index, determine, based on the cost indices, radio resource configurations for the plural network slices, and control the radio access network to provide radio resources to the plural network slices according to the determined radio resource configurations.

    NETWORK SLICING IN RADIO ACCESS NETWORK
    3.
    发明公开

    公开(公告)号:US20240196276A1

    公开(公告)日:2024-06-13

    申请号:US18516187

    申请日:2023-11-21

    CPC classification number: H04W28/16 H04W48/16

    Abstract: There is provided a method for radio access network slicing. A first set of radio access network, RAN, statistics and a second set of RAN statistics are received. The first set of RAN statistics comprises non real time statistics from the RAN. The second set of RAN statistics comprises near real time statistics from the RAN. The first set of RAN statistics and a service level agreement are provided to a non real time reinforcement learning model as input. Resource management policy per slice is obtained as output from the model. The second set of RAN statistics, the service level agreement and the resource management policy per slice are provided to a near real time reinforcement learning model as input. Resource allocation per slice is obtained as output from the model.

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