UE ML-Assisted RRM Testing with Warm-Up Signal Sequences
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Solution Overview
Problem
Current 3GPP tests are designed for deterministic outcomes, while RRM functionalities using ML assistance generate probabilistic outputs, and the performance of UEs with ML-assistance depends on historical measurements and past radio conditions, making it challenging to design a test framework that accurately evaluates UE behavior without explicitly testing the ML model implementation.
Innovation Solution
A method and apparatus for testing UE ML-assisted RRM functionalities by selecting the functionality to be tested, initializing the ML-assistance model, generating input test signals and reference output conditions, and activating the ML-assistance functionality, focusing on the output of the RRM functionality rather than the ML model implementation, to accommodate different UE implementations and ML architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current 3GPP test frameworks are used for deterministic outcomes, then test simplicity is maintained, but testing accuracy for ML-assisted RRM functionalities deteriorates
Solution Approach 1:
The patent segments the testing process into distinct phases: ML model initialization phase, warm-up phase for generating historical measurements, and actual functionality testing phase. This segmentation allows each phase to be handled with appropriate test procedures, improving accuracy without overwhelming complexity.
Solution Approach 2:
The patent implements preliminary actions by initializing the ML model with specific configurations and generating warm-up historical measurements before actual testing begins. This preliminary preparation ensures the ML model is in a proper state for accurate testing of RRM functionalities.
2Reliability
If ML model implementation is explicitly tested, then model-specific accuracy is improved, but adaptability across different UE implementations deteriorates
Solution Approach 1:
The patent extracts the ML model initialization and warm-up procedures as separate, standardized steps that can be applied uniformly across different UE implementations. The actual RRM functionality testing then focuses on model-agnostic behaviors, ensuring both reliability and adaptability.
Solution Approach 2:
The patent creates a universal test framework that handles multiple functions: ML model initialization, warm-up generation of historical measurements, and RRM functionality testing. This multi-functional approach ensures broad compatibility across different UE implementations while maintaining testing rigor.
3Reliability
If historical measurements and past radio conditions are considered in testing, then testing realism is improved, but test sequence complexity deteriorates
Solution Approach 1:
The patent implements periodic action through the warm-up phase, where historical measurements are generated over a defined period before actual testing. This periodic preparation creates realistic conditions without requiring continuous complex test sequence management throughout the entire testing process.
Data Source
AI summary
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.