WO2024028700 - ARTIFICIAL INTELLIGENCE FOR CHANNEL STATE INFORMATION

National phase entry is expected:
Publication Number WO/2024/028700
Publication Date 08.02.2024
International Application No. PCT/IB2023/057586
International Filing Date 26.07.2023
Title **
[English] ARTIFICIAL INTELLIGENCE FOR CHANNEL STATE INFORMATION
[French] INTELLIGENCE ARTIFICIELLE POUR INFORMATIONS D'ÉTAT DE CANAL
Applicants **
LENOVO (SINGAPORE) PTE. LTD.
Inventors
POURAHMADI, Vahid
HINDY, Ahmed
KOTHAPALLI, Venkata Srinivas
NANGIA, Vijay
BAGHERI, Hossein
Priority Data
63/394,814   03.08.2022   US
63/394,822   03.08.2022   US
63/394,857   03.08.2022   US
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Quotation for National Phase entry

Country StagesTotal
China Filing, Examination, Granting2868
EPO Filing, Examination, Granting12928
Japan Filing, Examination, Granting2307
South Korea Filing, Examination, Granting2445
USA Filing, Examination, Granting9990
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Total: 30,538

The term for entry into the National Phase has expired. This quotation is for informational purposes only

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Abstract[English] Various aspects of the present disclosure relate to methods, apparatuses, and systems that support artificial intelligence (AI) for channel state information (CSI). For instance, implementations provide an architecture and associated signaling for compressing an input (e.g., CSI at a user equipment (UE)), quantizing the compressed input, transmitting the quantized compressed input, and extracting (e.g., at a network entity such as a gNB) relevant information from the quantized compressed input. In at least some implementations the architecture includes one or more AI/machine learning (ML) models and is composed of multiple components, such as a UE component and a network entity component.[French] Divers aspects de la présente divulgation concernent des procédés, des appareils, et des systèmes qui prennent en charge une intelligence artificielle (AI) pour des informations d'état de canal (CSI). Par exemple, des mises en œuvre concernent une architecture et une signalisation associée pour compresser une entrée (par exemple, des CSI au niveau d'un équipement utilisateur (UE)), quantifier l'entrée compressée, transmettre l'entrée compressée quantifiée, et extraire (par exemple, au niveau d'une entité de réseau telle qu'un gNB) des informations pertinentes à partir de l'entrée compressée quantifiée. Dans au moins certaines mises en œuvre, l'architecture comprend un ou plusieurs modèles d'apprentissage automatique (ML)/IA et est composée de multiples composants, tels qu'un composant d'UE et un composant d'entité de réseau.