公开(公告)号：US20250141761A1

公开(公告)日：2025-05-01

申请号：US18930934

申请日：2024-10-29

Applicant: Nokia Solutions and Networks Oy

Inventor： Quan PHAM VAN ,  NAKJUNG CHOI ,  Ahan KAK ,  Huu Trung THIEU

IPC: H04L41/5009 ,  H04L41/14

Abstract: To enable end-to-end performance monitoring, different solutions to obtain end-to-end values for different key performance indicators are disclosed. For example, when key performance indicator data from a plurality of different domains is received, the key performance indicator data received may be adapted to be a time-series data stream. Then, values of key performance indicators may be extracted, per a domain, from the time-series data stream. An end-to-end value for a key performance indicator may be determined, based on the extracted values of the key performance indicator in the different domains. The end-to-end value may be used for end-to-end performance monitoring of a network.

公开(公告)号：US20240334396A1

公开(公告)日：2024-10-03

申请号：US18598082

申请日：2024-03-07

Applicant: Nokia Solutions and Networks Oy

Inventor： Ahan KAK ,  Archana BURA ,  Nakjung CHOI

IPC: H04W72/04

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.

公开(公告)号：US20240284436A1

公开(公告)日：2024-08-22

申请号：US18581672

申请日：2024-02-20

Applicant: Nokia Solutions and Networks Oy

Inventor： Ahan KAK ,  Heba ABDEEN ,  Gopalasingham ARAVINTHAN ,  Nak Jung CHOI

IPC: H04W72/12 ,  H04W24/02 ,  H04W72/0446

CPC classification number: H04W72/12 ,  H04W24/02 ,  H04W72/0446

Abstract: According to an aspect, there is provided an apparatus configured to perform the following. The apparatus hosts a plurality of applications for managing network functions of a radio access network and schedules them for one or more future time slots. The apparatus obtains one or more predicted future values of one or more network parameters of the radio access network for the one or more future time slots. The apparatus determines whether or not at least one conflict exists in the scheduling of the plurality of applications based on the one or more predicted future values of the one or more network parameters. In response to determining that the at least one conflict exists, the apparatus resolves the at least one conflict by adjusting scheduling of at least one application involved in the at least one conflict.

公开(公告)号：US20240196276A1

公开(公告)日：2024-06-13

申请号：US18516187

申请日：2023-11-21

Applicant: Nokia Solutions and Networks Oy

Inventor： Ahan KAK ,  Nakjung CHOI

IPC: H04W28/16 ,  H04W48/16

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.