公开(公告)号：US20250150400A1

公开(公告)日：2025-05-08

申请号：US18501312

申请日：2023-11-03

Applicant: Nokia Technologies Oy

Inventor： Alessandro LIETO ,  Mathieu BOUSSARD ,  Ilaria MALANCHINI ,  Qi LIAO

IPC: H04L47/24 ,  H04L43/0811 ,  H04L47/80

Abstract: Systems, methods, apparatuses, and computer program products for dependency-aware quality of service (QoS) profile provisioning. The method may include obtaining information on at least one application data flow, and dependency information between one application and at least one other application. The method may also include receiving, from a network, information on an updated quality of service profile for the at least one application, and information on another updated quality of service profile for the at least one other application. The updated quality of service profile and the another updated quality of service profile may be selected based on the dependency graph

公开(公告)号：US20230214648A1

公开(公告)日：2023-07-06

申请号：US17928712

申请日：2021-05-26

Applicant: NOKIA TECHNOLOGIES OY

Inventor： Qi LIAO ,  Fahad SYED MUHAMMAD ,  Veronique CAPDEVIELLE ,  Afef FEKI ,  Suresh KALYANASUNDARAM ,  Ilaria MALANCHINI

IPC: G06N3/08 ,  H04B7/06 ,  G06N3/045

CPC classification number: G06N3/08 ,  H04B7/0617 ,  G06N3/045

Abstract: A deep transfer reinforcement learning (DTRL) method based on transfer learning within a deep reinforcement learning (DRL) framework is provided to accelerate the GoB optimization decisions when experiencing environment changes in the same source radio network agent or when being applied from a source radio network agent to a target radio network agent. The transferability of the knowledge embedded in a pre-trained neural network model as a Q-approximator is exploited, and a mechanism to transfer parameters from a source agent to a target agent is provided, where the transferability criterion is based on the similarity measure between the source and target domain.