Antenna Alert Correlation Heatmap for False Positive Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for monitoring base station antenna health generate excessive false positive alerts due to simple static thresholds on key performance indicators, leading to wasted resources and delayed addressing of actual issues, resulting in poor customer experience and data loss.
Innovation Solution
A management system that processes antenna alert data using a correlation model and change point detection to generate a correlation heatmap matrix, calculate scores, and perform corrective actions, thereby distinguishing true issues from false positives and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple static thresholds on key performance indicators are used to monitor antenna health, then the monitoring system is simple and easy to implement, but excessive false positive alerts are generated
Solution Approach 1:
The patent transforms static threshold parameters into dynamic parameters that adapt based on historical data, time of day, day of week, and other contextual factors. This allows the monitoring system to distinguish between normal fluctuations and actual antenna failures, significantly reducing false positive alerts while maintaining system simplicity
Solution Approach 2:
The monitoring system transitions from static threshold checking to dynamic threshold adjustment based on learned patterns from historical data. The system continuously adapts its alerting behavior to match normal operational variations, thereby improving alert accuracy without increasing operational complexity
2Ease of operation
If technicians are dispatched to investigate false positive antenna alerts, then alert response is provided, but computing resources, networking resources, and technician time are wasted
Solution Approach 1:
The system implements feedback loops where technician responses to alerts are fed back into the learning model. When technicians determine an alert was a false positive, this information is used to refine future threshold adjustments, creating a self-improving system that reduces resource waste while maintaining effective response to genuine issues
Solution Approach 2:
The monitoring system performs self-adjustment of thresholds based on historical data and learned patterns, reducing the need for manual technician intervention for false positives. The system serves itself by automatically adapting to normal operational variations without requiring resource-intensive human investigation
3Loss of energy
If actual antenna issues are not timely addressed due to false positive alerts, then resource consumption is reduced, but customer experience deteriorates and data is lost
Solution Approach 1:
The dynamic parameter adjustment ensures that genuine antenna issues are not masked by normal operational fluctuations. By adapting thresholds based on learned patterns, the system maintains high sensitivity to actual problems while becoming tolerant of normal variations, ensuring timely detection and response to real issues
Data Source
AI summary
A device may receive antenna alert data associated with antennas of a plurality of base stations of a network, and may process the antenna alert data, with a correlation model, to generate a correlation heatmap matrix. The device may utilize a change point detection model to process the antenna alert data, associated with the correlation heatmap matrix, to determine change point metrics, and may calculate scores for the antenna alert data associated with the correlation heatmap matrix and the change point metrics. The device may perform one or more corrective actions based on the scores.