AI/ML Model Test Mechanism for Positioning Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a need for a test mechanism to validate the Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) inference of AI/ML models used in AI/ML-based positioning in communication networks, as existing methods lack defined procedures for verifying the accuracy of these inferences.
Innovation Solution
The proposed solution involves a test apparatus that transmits test configuration information to another apparatus, indicating a test mode for an AI/ML model related to transmission and reception units (TRPs). This apparatus receives predicted channel indicators from the second apparatus, which were derived using the AI/ML model, and compares them with test channel indicators to determine if the AI/ML model is validated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI/ML models are used for LOS/NLOS inference in positioning systems, then positioning accuracy can be improved, but there is a lack of defined test procedures to validate the model accuracy
Solution Approach 1:
The test procedure is segmented into distinct phases: configuration phase where test parameters are defined, execution phase where AI/ML model predictions are collected, and evaluation phase where predictions are compared against ground truth. This segmentation transforms the complex validation process into manageable, systematic steps that can be implemented and verified.
Solution Approach 2:
The patent applies preliminary action by pre-defining test configurations including ground truth channel indicators, TRP arrangements, and test parameters before actual model validation. This preparatory setup ensures that validation criteria are established in advance, enabling systematic comparison of AI/ML model predictions against known reference values.
2Reliability
If existing test methods are used without defined procedures, then implementation is simpler, but the accuracy and reliability of LOS/NLOS inference cannot be validated
Solution Approach 1:
The patent implements feedback mechanisms by comparing AI/ML model predictions against ground truth channel indicators and calculating accuracy metrics. This feedback loop enables continuous validation and assessment of model performance, ensuring reliability through systematic evaluation of prediction accuracy against known reference values.
Solution Approach 2:
The test mechanism utilizes parameter changes by varying test configurations such as TRP arrangements, channel conditions, and model parameters to comprehensively validate AI/ML model performance under different scenarios. This approach ensures robust reliability assessment across multiple operating conditions rather than single-point validation.
Data Source
AI summary
Example embodiments of the present disclosure are related to artificial intelligence/machine learning (AI/ML) model test. A first apparatus transmits test configuration information to a second apparatus, the test configuration information indicating a test mode of an AI/ML model with respect to at least one transmission and reception unit (TRP), the at least one TRP being arranged within an environment based on a test plan for at least one test channel indicator. The first apparatus receives, from the second apparatus, at least one predicted channel indicator for the at least one TRP, the at least one predicted channel indicator being derived by the second apparatus using the AI/ML model. The first apparatus determines a test result for the AI/ML model based on a comparison between the at least one predicted channel indicator and the at least one test channel indicator, the test result indicating whether the AI/ML model is validated.