WO2025248474 - EVENT-BASED REINFORCEMENT LEARNING FOR RRM PARAMETER OPTIMIZATION
National phase entry is expected:
Publication Number
WO/2025/248474
Publication Date
04.12.2025
International Application No.
PCT/IB2025/055534
International Filing Date
28.05.2025
Title **
[English]
EVENT-BASED REINFORCEMENT LEARNING FOR RRM PARAMETER OPTIMIZATION
[French]
APPRENTISSAGE PAR RENFORCEMENT BASÉ SUR UN ÉVÉNEMENT POUR OPTIMISATION DE PARAMÈTRES DE RRM
Applicants **
NOKIA TECHNOLOGIES OY
Inventors
SONG, Jian
FEKI, Afef
HÖHNE, Hans Thomas
ALI-TOLPPA, Janne
VEIJALAINEN, Teemu Mikael
DOSTI, Endrit
ALI, Samad
KHATIBI, Sina
Priority Data
20245700
31.05.2024
FI
Application details
| Total Number of Claims/PCT | * |
| Number of Independent Claims | * |
| Number of Priorities | * |
| Number of Multi-Dependent Claims | * |
| Number of Drawings | * |
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| Number of Pages with Drawings | * |
| Pages of Specification | * |
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| Number of Office Actions | * |
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International Searching Authority |
EPO
* |
| Recordal of a Change of the Applicant's Name/Address |
Change of Applicant's Name and Address
* |
| Type of Assignment |
The Standard Agent's Assignment
* |
| Applicant's Legal Status |
Legal Entity
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| * | |
| * | |
| * | |
| * | |
| * | |
| Entry into National Phase under |
Chapter I
* |
| Patent Delivery |
Send the Letters Patent by Courier
* |
| Translation |
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* The data is based on automatic recognition. Please verify and amend if necessary.
** IP-Coster compiles data from publicly available sources. If this data includes your personal information, you can contact us to request its removal.
Quotation for National Phase entry
| Country | Stages | Total | |
|---|---|---|---|
| China | Filing, Examination, Granting | 2365 | |
| EPO | Filing, Examination, Granting | 9463 | |
| Japan | Filing, Examination, Granting | 2181 | |
| South Korea | Filing, Examination, Granting | 2043 | |
| USA | Filing, Examination, Granting | 4740 |
Total:
20,792
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Abstract[English]
According to an aspect, there is provided an apparatus for performing the following. The apparatus maintains, in the at least one memory, a reinforcement learning, RL, model trained to determine one or more radio resource management, RRM, parameters of the apparatus based on one or more radio measurement metrics. The apparatus receives, from a network entity, at least one configuration message comprising one or more RL strategies. The one or more RL strategies comprise at least one of: one or more RL event triggering conditions for triggering an event of the RL model, or one or more RL event exit conditions for exiting an event of the RL model. The apparatus carries out optimization of the one or more RRM parameters using the RL model according to the one or more RL strategies.[French]
Selon un aspect, l'invention concerne un appareil pour mettre en œuvre ce qui suit. L'appareil maintient, dans l'au moins une mémoire, un modèle d'apprentissage par renforcement, RL, entraîné pour déterminer un ou plusieurs paramètres de gestion de ressources radio, RRM, de l'appareil sur la base d'une ou de plusieurs métriques de mesure radio. L'appareil reçoit, en provenance d'une entité de réseau, au moins un message de configuration comprenant une ou plusieurs stratégies RL. La ou les stratégies RL comprennent une ou plusieurs conditions de déclenchement d'événement RL pour déclencher un événement du modèle RL, et/ou une ou plusieurs conditions de sortie d'événement RL pour sortir d'un événement du modèle RL. L'appareil réalise une optimisation du ou des paramètres de RRM au moyen du modèle RL selon la ou les stratégies RL.