Traffic Suppression Turning Point Detection in Cellular Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting traffic suppression turning points in cellular networks are labor-intensive and time-consuming, relying on engineering rule-based approaches that require extensive analysis and are heavily dependent on engineer experience, making them inefficient for large networks or varying resource utilization conditions.
Innovation Solution
A computer-implemented method and apparatus that predict key performance indicator (KPI) values for traffic loads within an upper range based on measurement values from a lower range, calculate prediction errors, and identify traffic suppression conditions using criteria satisfied by these errors or slope changes, allowing for automated detection of traffic suppression turning points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If engineering rule-based approaches are used to detect traffic suppression turning points, then detection accuracy can be achieved, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system automatically detects traffic suppression turning points by having the network elements self-monitor and self-report KPI data, eliminating the need for manual engineering analysis. The automated system processes measurement values, calculates slopes, and identifies turning points without human intervention, thus maintaining detection accuracy while significantly reducing time consumption.
Solution Approach 2:
The patent replaces manual engineering rule-based analysis with an automated computational system that uses algorithms to process KPI measurement values and detect turning points. This substitution of mechanical/engineering methods with automated computing achieves the same detection accuracy while eliminating the labor-intensive and time-consuming nature of manual analysis.
2Difficulty of detecting and measuring
If engineering rule-based approaches are used to detect traffic suppression turning points, then detection capability is achieved, but the process requires extensive analysis and is heavily dependent on engineer experience
Solution Approach 1:
The system enables network elements to automatically perform detection functions by self-monitoring KPI data and self-identifying turning points based on predefined algorithms. This eliminates the need for engineer expertise and experience-dependent manual analysis, reducing detection complexity while maintaining capability.
Solution Approach 2:
The patent transforms the detection process from complex engineering judgment to simple parameter-based automated decision making. By monitoring changes in KPI measurement values and their slopes, the system converts subjective engineering analysis into objective parameter threshold comparisons, reducing complexity and eliminating dependence on engineer experience.
3Manufacturing precision
If manual analysis methods are used for traffic suppression detection, then detailed analysis can be performed, but efficiency is low for large networks
Solution Approach 1:
The automated system enables each network element to independently and simultaneously perform detailed analysis of its own KPI data, allowing parallel processing across the entire network. This maintains analysis precision for each element while achieving high overall detection efficiency that scales with network size, unlike sequential manual analysis.
Solution Approach 2:
The patent divides the network into independent monitoring units where each element's KPI data is analyzed separately and simultaneously. This segmentation allows detailed analysis to be performed on each segment while the overall system achieves high productivity through parallel processing, solving the contradiction between analysis precision and detection efficiency for large networks.
Data Source
AI summary
Methods and apparatus for detecting a traffic suppression turning point in a communications system based on traffic behavior are provided. Models representing relationship between traffic loads and a key performance indicator of a cell or a cluster of cells may be built and tested to generate a set of prediction errors corresponding to a plurality of traffic load ranges. The prediction errors are examined against a criteria to determine a traffic suppression turning point in terms of traffic loads. The models built may also be used to calculate a set of KPI slope values corresponding to different traffic load ranges. The set of KPI slope values are examined against a criteria to determine a traffic suppression turning point in terms of traffic loads.