Cell Degradation Detection via Correlation Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting cell degradation in wireless communications networks are plagued by high false positives, computational complexity, and require significant human intervention, making them inefficient and unreliable.
Innovation Solution
A method using a first network node to determine cell degradation by comparing observed performance values with generated degraded patterns through correlation analysis, focusing on similarities rather than dissimilarities, and employing a dynamic threshold to reduce false positives and computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional threshold-based monitoring is used to detect cell degradation, then the detection mechanism is simple to implement, but it produces high false positives and cannot detect local degradations within normal ranges
Solution Approach 1:
The patent pre-generates multiple degraded patterns representing different types of cell degradations before actual detection occurs. These patterns are stored and used as reference templates during runtime, allowing the system to quickly compare observed values against known degradation signatures without complex real-time analysis
Solution Approach 2:
The patent creates simplified copies of degraded cell states in the form of pattern templates. Instead of analyzing complex raw data in real-time, the system compares observed performance values against these pre-created pattern copies, significantly reducing computational complexity while maintaining detection accuracy
2Reliability
If adaptive algorithms with learning stages are used to reduce false positives, then detection accuracy improves, but computational complexity and processing time increase significantly
Solution Approach 1:
The learning and pattern generation process is performed in advance during system initialization or offline stages. The pre-generated degraded patterns capture the essential characteristics of various failure modes without requiring real-time computation, enabling fast detection during actual operation
Solution Approach 2:
The patent extracts only the essential features of degradation patterns that are necessary for detection, storing them in compact template form. This extraction eliminates unnecessary computational overhead while retaining the key characteristics needed for accurate degradation detection
3Reliability
If multiple performance indicators are monitored to improve detection reliability, then more degradation types can be detected, but the number of thresholds to configure and false alarms increase
Solution Approach 1:
The patent creates a universal detection framework where a single correlation-based mechanism can detect multiple types of cell degradations across different performance indicators. The pre-generated degraded patterns serve as multi-functional templates that can match various degradation scenarios without requiring separate configuration for each indicator
Solution Approach 2:
The system changes the approach from fixed threshold parameters to pattern-based correlation parameters. Instead of configuring multiple thresholds for different indicators, the system uses correlation coefficients against pre-generated patterns, automatically adapting to different degradation types without manual parameter adjustment
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
A method performed by a first network node (110) for determining whether a performance of a cell (130) associated with a second network node (120) is degraded or not. The first network node (110) and the second network node (120) operate in a wireless communications network (100). The first network node (110) obtains(201) a first set of values making up a pattern comprising a set of values indicative of a performance of the cell (130). The first network node (110) then determines(204) a first correlation between the obtained first set of values and a generated set of values. The generated set of values is indicative of a degraded performance of the cell (130). The first network node (110) then determines(206) whether the performance of the cell (130) is degraded or not based on the determined first correlation, with respect to a first threshold.