QoE Analysis Using Trace File Correlation
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Solution Overview
Problem
Modern telecommunications networks face service degradation due to data traffic congestion, leading to issues like data packet loss, queuing delay, and inability to establish connections, which degrade Quality of Service (QoS) and Quality of User Experience (QoE) for end users.
Innovation Solution
A method that collects and correlates trace files from multiple nodes in the network to identify the root cause of service degradation by logging data packet identifications with timestamps, analyzing performance metrics across different layers of the communication protocol stack, and providing alerts and recommendations for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data traffic demand increases to meet user needs, then service coverage and user access expand, but data traffic congestion occurs leading to packet loss and queuing delay
Solution Approach 1:
The system performs preliminary actions by collecting trace files from multiple network nodes before service degradation becomes apparent to users. By proactively gathering data packets, timestamps, and performance metrics from various layers of the communication protocol stack, the system can analyze and identify potential congestion issues before they significantly impact QoS and QoE, allowing preventive optimization measures to be taken
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring network performance through trace file collection and analysis. The correlation analysis of trace files from multiple nodes provides feedback on network health, packet loss rates, and queuing delays, enabling the system to identify root causes of service degradation and recommend or implement optimization measures to improve data transmission reliability
2Measurement precision
If network monitoring and analysis capabilities are enhanced to identify root causes, then service optimization improves, but system complexity increases
Solution Approach 1:
The system applies segmentation by collecting trace files from multiple discrete network nodes separately and analyzing different layers of the communication protocol stack independently. Each node's trace files are processed individually to extract specific performance metrics, and then these segmented results are correlated to identify the root cause of service degradation, making the complex analysis manageable and systematic
Solution Approach 2:
The system introduces an intermediary analysis layer that correlates trace files from multiple network nodes. This intermediary correlation process acts as a mediator between the raw data collected from various nodes and the final root cause identification, simplifying the complexity by providing a structured approach to analyze and correlate data from multiple sources without requiring direct complex interactions between all nodes
Data Source
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AI summary
The techniques described herein involve analysis of client device Quality of Experience diagnostic files including an operations log or diagnostic files for a client device. The client device Quality of Experience diagnostic files may be generated by a client device and sent to a network node for analysis. The diagnostic files may be analyzed to determine device Key Performance Indicators and a device Quality of Experience, and to determine a root cause of a network problem (such as dropped calls) leading to a diminished Quality of Experience. In some embodiments, the diagnostic files may be aggregated to form a database of aggregated diagnostics, which can be used to further analyze a network to determine the root cause of a network problem. In some embodiments, the aggregated diagnostics may be indexed according to location, time, device type, device problem, or access technology.