Machine learning model configuration in wireless networks

US12089291B2 · kind B2 · utility

Inventors

• Xipeng Zhu · Beijing, CN
• Gavin Bernard Horn · Village of La Jolla, US
• Taesang Yoo · Riverside, US
• Rajeev Kumar · San Diego, US
• Shankar Krishnan · Santa Clara, US
• Aziz Gholmieh · San Diego, US
• Rajat Prakash · San Diego, US
• Eren BALEVI · San Diego, US

Key dates

• Filing date: Jun 15, 2021
• Grant date: Sep 10, 2024
• Expiry date: Jul 6, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/084
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Certain aspects of the present disclosure provide techniques and apparatus for determining neural network functions (NNFs) and configuring and using corresponding machine learning (ML) models for performing one or more ML-based wireless communications management procedures. An example method performed by a user equipment includes transmitting, to a base station (BS), UE capability information indicating at least one radio capability of the UE and at least one machine learning (ML) capability of the UE and receiving, from the BS based on the UE capability information, ML configuration information indicating at least one neural network function (NNF) and at least one ML model corresponding to the at least one NNF.