Validating ML Line-of-Sight Predictions via Reference Signal Comparison
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Solution Overview
Problem
Machine learning (ML) models used to determine line-of-sight (LOS) indicator values in wireless communications often produce inaccurate results, leading to uncertainty and prediction errors.
Innovation Solution
A method is introduced to validate ML-based predictions of LOS indicator values by comparing them with ground truth values determined independently of ML. This involves transmitting reference signals to a device under test (DUT), determining LOS indicator values using both ML and a known accuracy method, and calculating error values to assess the ML model's conformance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used to determine LOS indicator values, then automation and productivity are improved, but measurement precision and reliability deteriorate due to inaccurate predictions and uncertainty
Solution Approach 1:
The patent implements a feedback mechanism where ML model predictions are continuously validated against ground truth values obtained from actual measurements. Error values are calculated and used to refine the ML models, creating a closed-loop system that improves prediction accuracy while maintaining automation. This resolves the contradiction by enabling automated determination with improving precision through iterative validation and refinement.
Solution Approach 2:
The patent introduces an intermediary validation system that acts as a mediator between ML model predictions and final LOS indicator values. The validation system compares ML predictions with ground truth measurements, calculates error metrics, and determines whether predictions pass conformance thresholds. This intermediary layer maintains automation while ensuring measurement precision through systematic validation.
2Productivity
If machine learning models are used to determine LOS indicator values, then productivity is improved, but reliability worsens due to prediction errors and uncertainty
Solution Approach 1:
The patent applies preliminary action by pre-validating ML models against ground truth data before deployment and establishing conformance thresholds in advance. Validation frameworks and error metrics are predetermined, allowing rapid automated determination while ensuring reliability through pre-established validation criteria. This resolves the contradiction by preparing validation mechanisms beforehand, enabling both speed and reliability.
Solution Approach 2:
The feedback mechanism continuously monitors prediction accuracy and compares ML model outputs with ground truth values. Error values are calculated and fed back to determine whether predictions pass conformance tests, ensuring reliable results while maintaining high productivity through automated validation processes.
3Measurement precision
If validation testing is performed to ensure ML model accuracy, then measurement precision is improved, but device complexity and loss of time increase
Solution Approach 1:
The patent uses copying by creating simplified validation frameworks that replicate essential validation logic without requiring complex infrastructure. Ground truth values are obtained through controlled measurements, and validation processes copy proven methodologies from established testing frameworks. This reduces device complexity while maintaining measurement precision through validated approaches.
Solution Approach 2:
The patent applies parameter changes by establishing configurable conformance thresholds and validation parameters that can be adjusted based on application requirements. By making validation parameters configurable rather than fixed, the system achieves measurement precision while allowing flexibility that reduces overall system complexity. Different scenarios can use appropriate parameter settings without requiring complex adaptive mechanisms.
4Reliability
If comprehensive validation is performed on ML models, then reliability is improved, but loss of time increases due to testing and error calculation
Solution Approach 1:
The patent applies partial action by implementing selective validation that focuses on critical performance metrics and conformance thresholds rather than exhaustive testing of all possible scenarios. Validation is performed on key parameters that most impact reliability, achieving sufficient confidence in results while minimizing time loss. This resolves the contradiction by validating only the most essential aspects that ensure reliability.
Solution Approach 2:
Comprehensive validation frameworks are established in advance with predetermined conformance thresholds and error metrics. By preparing validation criteria beforehand, the system can quickly assess ML model predictions without time-consuming ad hoc analysis, achieving reliability while minimizing time loss through pre-planned validation procedures.
Data Source
AI summary
Test equipment (TE) may, while in a test configuration, cause transmission of a first reference signal for receipt by a communication device under test (DUT). The TE may receive a first line-of-sight indicator value as determined by the DUT in accordance with a method having a known accuracy. While in the test configuration, the TE may cause transmission of a second reference signal for receipt by the DUT. The TE may receive a second line-of-sight indicator value as determined by the DUT based at least in part on a trained machine learning (ML) model. The TE may determine an error value between the first line-of-sight indicator value and the second line-of-sight indicator value. Based at least in part on the error value, the TE may determine whether the trained ML model passes a conformance test related to estimation of line-of-sight indicator values by the trained ML model.