Telemetry Quality Profiling for Accurate Wireless RRM Computation
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Solution Overview
Problem
Erroneous radio resource management (RRM) computations due to poor quality telemetry data can adversely impact wireless network performance, often resulting from malicious or anomalous data injection.
Innovation Solution
Implement real-time telemetry quality tracking and profiling using artificial intelligence (AI) and machine learning (ML) to generate a data-quality score, which assesses the reliability of telemetry data and filters or corrects anomalous data, ensuring accurate RRM computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If RRM computations are performed using all available telemetry data, then system capacity and automation are improved, but reliability deteriorates due to erroneous computations from poor quality data
Solution Approach 1:
The patent applies preliminary action by performing data quality assessment and statistical profiling before RRM computations are executed. The system continuously monitors telemetry data quality metrics and pre-identifies poor quality data segments, ensuring that only validated data is used in subsequent RRM calculations. This prevents erroneous computations from propagating through the system while maintaining continuous operation.
Solution Approach 2:
The patent introduces an intermediary data quality assessment layer between telemetry data collection and RRM computation. This intermediary component evaluates data quality scores, applies statistical profiles, and determines which data segments are suitable for RRM processing. It acts as a mediator that filters and validates data without stopping the overall RRM workflow, thus maintaining productivity while improving reliability.
2Reliability
If telemetry data quality monitoring is implemented, then reliability is improved, but device complexity increases due to additional processing layers
Solution Approach 1:
The patent applies universality by designing the data quality monitoring system to perform multiple functions simultaneously. The same statistical profiling mechanism is used for both data validation and performance analysis, and the quality assessment framework serves both filtering purposes and diagnostic purposes. This multi-functionality reduces the need for separate dedicated components, thereby limiting the increase in device complexity.
Solution Approach 2:
The system implements self-service through automated statistical profiling and quality assessment that operates without manual intervention. The data quality monitoring framework automatically adapts to different data types and sources, self-calibrates thresholds based on historical patterns, and dynamically adjusts validation rules. This automation reduces the operational complexity burden despite adding processing layers.
3Measurement precision
If statistical profiling and data-quality modeling are applied in real-time, then measurement precision is improved, but use of energy increases due to continuous processing
Solution Approach 1:
The patent applies periodic action by implementing continuous but rhythmically structured data quality monitoring. Instead of processing every single data point with full statistical profiling, the system periodically samples data segments, applies comprehensive statistical analysis at intervals, and uses lighter-weight validation between intervals. This periodic approach maintains measurement precision while significantly reducing average energy consumption compared to continuous full-analysis processing.
Data Source
AI summary
A system and method are provided for tracking the quality of telemetry data in a wireless network, and to provide profiling of the telemetry to prevent erroneous radio resource management (RRM) computations. Statistical profiles are generated from the telemetry data that includes both computation data, which is used in RRM computations, and other network data. A data-quality score is generated based on the other data and statistical profiles of the computation data. The data-quality score represents whether the telemetry data is of sufficient quality to be used in RRM computations. The data-quality score can be based, at least in part, on detecting changes in the statistical profiles relative to a baseline statistical profile of the telemetry data and using the second network data to assess a likelihood that the detected changes arise from a degradation in a quality of the first network data.