Virtual Network Assistant for High-Confidence SLE Root Cause Detection
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Solution Overview
Problem
Existing wireless networks face challenges in accurately identifying the root cause of System Level Experience (SLE) degradation, which can lead to incorrect corrective measures that adversely impact users.
Innovation Solution
A system that continuously monitors SLE indicators and network components, calculates probabilities and mutual information to identify the most likely root cause of SLE degradation, and initiates targeted corrective actions with high confidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If corrective measures are initiated without high confidence in root cause identification, then response speed is improved, but system reliability deteriorates due to incorrect actions impacting proper users
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring SLE indicators and calculating probabilities of potential root causes before initiating any corrective measures. This advance preparation ensures that when corrective action is needed, the system has already identified the most likely culprit with high confidence, thus maintaining both fast response and high reliability
Solution Approach 2:
The system implements feedback loops where corrective actions are triggered only when the calculated probability of a root cause exceeds a confidence threshold. The system continuously monitors SLE indicators, updates probability calculations, and adjusts corrective actions based on this feedback, ensuring reliable intervention only when sufficiently confident
2Measurement precision
If comprehensive monitoring of all network components is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the complex network monitoring task into distinct components: SLE indicator collection, probability calculation for each potential root cause, and corrective action determination. By dividing the monitoring scope into manageable segments with specific probability thresholds, the system achieves comprehensive monitoring without being overwhelmed by complexity
Solution Approach 2:
The system changes parameters by focusing on specific SLE indicators and their probability distributions rather than monitoring all possible network parameters simultaneously. This selective parameter monitoring maintains high measurement precision while reducing system complexity through targeted observation
Data Source
AI summary
Methods and apparatus for identifying the root cause of deterioration of system level experience (SLE). Offending network components that caused the SLE deterioration are identified and corrective actions are taken.