Intelligent Diagnostic Data Collection Framework
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Solution Overview
Problem
Current diagnostic data collection methods in wireless communication networks are inefficient, leading to high call failure ratios and user experience impairments, as they collect data blindly and frequently, causing network bandwidth overhead and battery drain, and result in limited performance improvements.
Innovation Solution
A framework for intelligent diagnostic data collection using a network element with a processor that determines a spatiotemporal correlation model to strategically collect new diagnostic data, reducing unnecessary measurements and optimizing data collection based on spatial and temporal correlations, thereby minimizing network bandwidth usage and maintaining monitoring granularity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diagnostic data is collected frequently and blindly, then measurement precision is improved, but network bandwidth overhead increases and device battery drains
Solution Approach 1:
The patent implements dynamic data collection by adjusting collection frequency and locations based on detected environmental changes. The system transitions from static frequent collection to adaptive collection, where measurement intervals are modified according to actual network conditions, thereby reducing unnecessary energy consumption while maintaining measurement precision when needed.
Solution Approach 2:
The system changes collection parameters (frequency, location, type of data) based on detected environmental conditions. When the environment is stable, collection frequency is reduced; when changes are detected, collection intensity increases. This parameter adaptation resolves the contradiction by making collection efficiency dependent on actual diagnostic needs.
2Productivity
If diagnostic data is collected frequently, then troubleshooting efficiency is improved, but network bandwidth overhead increases
Solution Approach 1:
The patent extracts only the essential diagnostic data needed for troubleshooting by using correlation models to identify which measurements provide meaningful information. Instead of collecting all possible data frequently, the system extracts and collects only those data points that contribute to diagnostic accuracy, thereby improving troubleshooting efficiency while reducing network bandwidth consumption.
Solution Approach 2:
The system applies partial action by collecting diagnostic data selectively rather than comprehensively. Using spatiotemporal correlation models, it determines that only partial data collection at specific locations and times is sufficient for effective troubleshooting, avoiding the excessive bandwidth usage associated with complete frequent data collection across all network elements.
3Measurement precision
If more diagnostic data is collected, then diagnostic accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing spatiotemporal correlation models that encode relationships between different diagnostic data points. These models are built in advance based on historical data and environmental understanding, allowing the system to make accurate diagnostic inferences from limited new measurements without requiring complex real-time processing of large datasets.
Solution Approach 2:
The correlation models serve as intermediaries between raw diagnostic data and diagnostic conclusions. Instead of directly processing complex relationships between numerous data points, the system uses pre-computed correlation models as mediators that translate limited measurements into accurate diagnostic information, thereby reducing processing complexity while maintaining diagnostic accuracy.
Data Source
AI summary
A method for collecting diagnostic data in a wireless communication network. The method comprises storing previously measured results of the diagnostic data for improving quality of the wireless communication network, determining a spatiotemporal correlation model based on the previously measured results of the diagnostic data in accordance with a data collection strategy, and collecting new diagnostic data based on the determined spatiotemporal correlation model and the data collection strategy.