18259132. METHODS AND APPARATUSES FOR TESTING USER EQUIPMENT (UE) MACHINE LEARNING-ASSISTED RADIO RESOURCE MANAGEMENT (RRM) FUNCTIONALITIES simplified abstract (Nokia Technologies Oy)

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METHODS AND APPARATUSES FOR TESTING USER EQUIPMENT (UE) MACHINE LEARNING-ASSISTED RADIO RESOURCE MANAGEMENT (RRM) FUNCTIONALITIES

Organization Name

Nokia Technologies Oy

Inventor(s)

[[:Category:István Zsolt Kov�cs of Aalborg (DK)|István Zsolt Kov�cs of Aalborg (DK)]][[Category:István Zsolt Kov�cs of Aalborg (DK)]]

Teemu Mikael Veijalainen of Helsinki (FI)

Wolfgang Zirwas of Munich (DE)

Navin Hathiramani of Coppell TX (US)

[[:Category:Hans Thomas H�hne of Helsinki (FI)|Hans Thomas H�hne of Helsinki (FI)]][[Category:Hans Thomas H�hne of Helsinki (FI)]]

METHODS AND APPARATUSES FOR TESTING USER EQUIPMENT (UE) MACHINE LEARNING-ASSISTED RADIO RESOURCE MANAGEMENT (RRM) FUNCTIONALITIES - A simplified explanation of the abstract

This abstract first appeared for US patent application 18259132 titled 'METHODS AND APPARATUSES FOR TESTING USER EQUIPMENT (UE) MACHINE LEARNING-ASSISTED RADIO RESOURCE MANAGEMENT (RRM) FUNCTIONALITIES

Simplified Explanation

The abstract describes a method for testing machine learning-assisted radio resource management functionalities in user equipment.

  • Select a radio resource management functionality to be tested for a user equipment with machine learning-assistance capabilities.
  • Initialize a machine learning-assistance model in the user equipment based on the advertised machine learning-assistance capabilities.
  • Generate input test signals and corresponding reference output test conditions based on the machine learning-assistance radio resource management functionality under test.
  • Activate UE machine learning-assistance functionality and provide a test sequence with the generated input test signals and corresponding reference output conditions.

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      1. Potential Applications
  • Testing and optimizing machine learning-assisted radio resource management functionalities in user equipment.
  • Improving network performance and efficiency through machine learning-assisted RRM testing.
      1. Problems Solved
  • Ensuring the proper functioning and performance of machine learning-assisted RRM functionalities in user equipment.
  • Identifying and addressing any issues or limitations in the machine learning-assistance models.
      1. Benefits
  • Enhanced network performance and resource management.
  • Improved user experience through optimized radio resource allocation.
  • Efficient testing and validation of machine learning-assisted RRM functionalities in user equipment.


Original Abstract Submitted

Systems, methods, apparatuses, and computer program products for testing user equipment (UE) machine learning-assisted radio resource management (RRM) functionalities are provided. One method may include selecting a radio resource management (RRM) functionality to be tested for a user equipment (UE) having advertised machine learning (ML)-assistance capabilities, initializing a machine learning (ML)-assistance model in the user equipment based on the advertised machine learning (ML)-assistance capabilities, generating one or more input test signals and corresponding reference output test conditions depending on the machine learning (ML)-assistance radio resource management (RRM) functionality under test, and activating UE machine learning (ML)-assistance functionality and provisioning, to the user equipment, a test sequence with the generated input test signals and corresponding reference output conditions.

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