18446951. MACHINE LEARNING MODEL VALIDATION FOR UE POSITIONING BASED ON REFERENCE DEVICE INFORMATION FOR WIRELESS NETWORKS simplified abstract (Nokia Technologies Oy)

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MACHINE LEARNING MODEL VALIDATION FOR UE POSITIONING BASED ON REFERENCE DEVICE INFORMATION FOR WIRELESS NETWORKS

Organization Name

Nokia Technologies Oy

Inventor(s)

Muhammad Ikram Ashraf of Espoo (FI)

Teemu Mikael Veijalainen of Espoo (FI)

[[:Category:Mikko S�ily of Espoo (FI)|Mikko S�ily of Espoo (FI)]][[Category:Mikko S�ily of Espoo (FI)]]

Oana-Elena Barbu of Aalborg (DK)

Taylan Sahin of Munich (DE)

Afef Feki of Massy (FR)

Athul Prasad of Naperville IL (US)

MACHINE LEARNING MODEL VALIDATION FOR UE POSITIONING BASED ON REFERENCE DEVICE INFORMATION FOR WIRELESS NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18446951 titled 'MACHINE LEARNING MODEL VALIDATION FOR UE POSITIONING BASED ON REFERENCE DEVICE INFORMATION FOR WIRELESS NETWORKS

Simplified Explanation

The abstract describes a method where a first user device receives positioning measurement reports and reference positioning-related information from a network node or a second user device. The method involves using a machine learning model to determine estimated positioning-related information based on the positioning measurement reports, and then evaluating the performance of the machine learning model based on the reference information and the estimated information.

  • The method involves receiving positioning measurement reports and reference positioning-related information.
  • A machine learning model is used to determine estimated positioning-related information.
  • The performance of the machine learning model is evaluated based on the reference information and the estimated information.
  • Actions are performed based on the performance indication.

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      1. Potential Applications
  • Location-based services
  • Indoor navigation systems
  • Autonomous vehicles
  • Asset tracking systems
      1. Problems Solved
  • Improving accuracy of positioning systems
  • Enhancing performance of machine learning models in positioning applications
  • Streamlining testing and validation processes for positioning technologies
      1. Benefits
  • Increased accuracy in determining positioning-related information
  • Efficient testing and validation of machine learning models
  • Enhanced performance of location-based services and navigation systems


Original Abstract Submitted

A method may include receiving, by a first user device from a network node or a second user device, 1) a positioning measurement report including at least one positioning measurement measured by a reference device, and 2) reference positioning-related information to be used for testing and/or validating a machine learning model; determining estimated positioning-related information as outputs of the machine learning model based on at least a portion of the positioning measurement report as inputs to the machine learning model; determining a performance indication of the machine learning model based on the reference positioning-related information and the estimated positioning-related information, wherein the performance indication indicates a performance or accuracy of the machine learning model; and performing, by the first user device, an action based on the performance indication.

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