公开(公告)号：US20240267851A1

公开(公告)日：2024-08-08

申请号：US18567199

申请日：2022-06-20

Applicant: NOKIA SOLUTIONS AND NETWORKS OY

Inventor： István Zsolt KOVÁCS ,  Klaus Ingemann PEDERSEN ,  Jian SONG ,  Muhammad Majid BUTT

IPC: H04W52/14 ,  H04B17/318 ,  H04W24/08 ,  H04W52/24

CPC classification number: H04W52/146 ,  H04B17/328 ,  H04W24/08 ,  H04W52/242

Abstract: According to an example embodiment, a network node device is configured to generate at least one client cluster comprising at least one client device served by the network node device according to at least one clustering criterion; assign the at least one control algorithm instance to the at least one client cluster; control at least one transmission power control parameter of the at least one client device in the at least one client cluster using the at least one control algorithm instance.

公开(公告)号：US20250126677A1

公开(公告)日：2025-04-17

申请号：US18912137

申请日：2024-10-10

Applicant: Nokia Solutions and Networks Oy

Inventor： Jian SONG ,  Stefano PARIS ,  Andrea MARCANO ,  Afef FEKI

IPC: H04W76/28 ,  H04W52/02

Abstract: A RL agent performs a RL process to configure at least one Discontinuous Reception, DRX, cycle for a User Equipment, UE. An action is selected by the RL agent in an action space. Each action in the action space corresponds to a DRX cycle configuration. The RL agent sends to the UE indication to use the DRX cycle configuration corresponding to the selected action. The RL agent receives state information computed over at least one DRX cycle configured based on a DRX cycle configuration indicated by the RL agent. The RL agent computes a reward on the basis of the state information.

公开(公告)号：US20250097093A1

公开(公告)日：2025-03-20

申请号：US18729676

申请日：2023-01-20

Applicant: NOKIA SOLUTIONS AND NETWORKS OY

Inventor： Muhammad Majid BUTT ,  István Zsolt KOVÁCS ,  Jian SONG ,  Klaus Ingemann PEDERSEN

IPC: H04L41/042 ,  G06N20/00 ,  H04L41/16

Abstract: A machine-learning (ML) orchestrator entity provides distributed, flexible, and efficient parameter initialization and updating for ML agents can be installed on network nodes operating under similar radio conditions. The ML orchestrator entity instructs each of such network nodes to iteratively run the ML agent in a training mode. Each run yields a local set of parameters for the ML agent. After each run, the ML orchestrator entity collects and uses the local sets of parameters from two or more network nodes to derive a common set of parameters for the network nodes. The ML orchestrator further instructs each of the network nodes to update its own local set of parameters based on the common set of parameters and use the updated local set of parameters in a subsequent run. The ML orchestrator entity repeats these steps until a termination criterion for the training mode is met.

公开(公告)号：US20250031065A1

公开(公告)日：2025-01-23

申请号：US18710244

申请日：2022-12-15

Applicant: NOKIA SOLUTIONS AND NETWORKS OY

Inventor： István Zsolt KOVÁCS ,  Muhammad Majid BUTT ,  Jian SONG ,  Klaus Ingemann PEDERSEN

IPC: H04W24/02 ,  H04L41/16

Abstract: The present disclosure relates to a machine-learning (ML) orchestrator entity that provides distributed, flexible, and efficient parameter initialization for ML agents installed on network nodes operating under similar radio conditions. For this end, the ML orchestrator entity instructs two or more of the network nodes to run two or more ML agents in a training mode, which results in generating two or more sets of parameters. Then, the ML orchestrator entity uses the sets of parameters to derive a common set of parameters for the network nodes. The common set of parameters is to be used in an inference mode of the ML agent at each of the network nodes. The transmission of the common set of parameters to the network nodes may be subsequently initiated by the ML orchestrator entity itself or by each of the network nodes independently.