WO2025150017 - ENHANCING TRAINING DATASETS FOR CHANNEL STATE INFORMATION (CSI) FEEDBACK
National phase entry is expected:
Publication Number
WO/2025/150017
Publication Date
17.07.2025
International Application No.
PCT/IB2025/051653
International Filing Date
14.02.2025
Title **
[English]
ENHANCING TRAINING DATASETS FOR CHANNEL STATE INFORMATION (CSI) FEEDBACK
[French]
AMÉLIORATION D'ENSEMBLES DE DONNÉES D'APPRENTISSAGE POUR RETOUR D'INFORMATIONS D'ÉTAT DE CANAL (CSI)
Applicants **
LENOVO (SINGAPORE) PTE. LTD.
Inventors
BARZEGAR KHALILSARAI, Mahdi
POURAHMADI, Vahid
Priority Data
63/554,591
16.02.2024
US
Application details
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International Searching Authority |
EPO
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| Recordal of a Change of the Applicant's Name/Address |
Change of Applicant's Name and Address
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| Type of Assignment |
The Standard Agent's Assignment
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| Applicant's Legal Status |
Legal Entity
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| Entry into National Phase under |
Chapter I
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| Patent Delivery |
Send the Letters Patent by Courier
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| Translation |
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Quotation for National Phase entry
| Country | Stages | Total | |
|---|---|---|---|
| China | Filing, Examination, Granting | 2298 | |
| EPO | Filing, Examination, Granting | 11160 | |
| Japan | Filing, Examination, Granting | 2309 | |
| South Korea | Filing, Examination, Granting | 2385 | |
| USA | Filing, Examination, Granting | 5340 |
Total:
23,492
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Abstract[English]
Various aspects of the present disclosure relate to Artificial Intelligence (AI) and/or Machine Learning (ML)-based training models for channel state information (CSI) feedback. For example, systems and methods may employ dithering terms to instances of ground truth datasets at a secondary node (e.g., a base station), which receives parameters, along with quantized datasets, from a primary node (e.g., a UE) that performed CSI measurements. The secondary node may construct the training datasets using generated dithering signals and the received datasets.[French]
Divers aspects de la présente divulgation concernent des modèles d'apprentissage basés sur l'intelligence artificielle (IA) et/ou l'apprentissage automatique (ML) pour un retour d'informations d'état de canal (CSI). Par exemple, des systèmes et des procédés peuvent employer des termes de tramage pour des instances d'ensembles de données de réalité de terrain au niveau d'un nœud secondaire (par exemple, une station de base), qui reçoit des paramètres, conjointement avec des ensembles de données quantifiés, en provenance d'un nœud primaire (par exemple, un UE) qui a effectué des mesures de CSI. Le nœud secondaire peut construire les ensembles de données d'apprentissage à l'aide de signaux de tramage générés et des ensembles de données reçus.