Data Analytics-Based SLS Assurance for Automatic QoE Reporting
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Solution Overview
Problem
Existing QoE measurement collection procedures in 5G networks are inefficient and unable to automatically adjust network configurations to ensure Service Level Specifications (SLS), and they do not adequately cover all service types, leading to suboptimal user experience.
Innovation Solution
A data analytics-based closed-loop SLS assurance mechanism that automatically triggers QoE measurement reporting and network configuration adjustments using UE, RAN, and core network entities, employing machine learning and AI to analyze QoE data for timely optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If automatic QoE measurement reporting is implemented, then network reaction speed to QoE degradation is improved, but system complexity increases
Solution Approach 1:
The UE autonomously performs QoE measurements and automatically reports them to the network when degradation is detected, without requiring manual intervention or complex centralized control. The network side simply receives and processes these self-generated reports, significantly reducing system complexity while maintaining fast response speed.
Solution Approach 2:
QoE measurement configurations and reporting criteria are pre-defined and stored in the UE before actual service delivery. When service conditions are met, the UE automatically triggers reporting based on these pre-configured parameters, enabling rapid network reaction without real-time decision complexity.
2Measurement precision
If comprehensive QoE measurements are collected, then measurement accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts and reports only the most critical QoE parameters and degradation indicators to the network, rather than transmitting all possible measurement data. This selective reporting approach maintains measurement accuracy for key metrics while significantly reducing data processing complexity on the network side.
Solution Approach 2:
Different QoE parameters are measured and reported with different levels of detail and frequency based on their importance to service quality. Critical parameters are monitored continuously with high precision, while less important parameters use simplified measurement approaches, optimizing the balance between accuracy and processing complexity.
3Reliability
If continuous QoE monitoring is performed, then service assurance reliability is improved, but energy consumption increases
Solution Approach 1:
QoE measurements are performed periodically at optimized intervals rather than continuously, with the reporting frequency dynamically adjusted based on service conditions and degradation detection. This periodic monitoring approach maintains service assurance reliability while significantly reducing energy consumption compared to continuous monitoring.
Solution Approach 2:
The system performs comprehensive QoE measurements only when degradation is detected or during critical service phases, using simplified or reduced monitoring during normal operation. This partial action approach ensures reliable service assurance when needed while minimizing energy consumption during stable conditions.
Data Source
AI summary
An operations, administration and maintenance (OAM) function provides service level specification (SLS) assurance by executing a method. The method includes: determining at least one policy for SLS assurance for a user equipment (UE) to be used in automatic quality of experience (QoE) measurement reporting, the at least one policy comprising an associated QoE measurement reporting criterion and a reporting format; and transmitting the at least one policy towards the UE.