5G Network Slice Quality Assurance for Transport Latency
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Solution Overview
Problem
Current service provider operational practices are inefficient in addressing QoS and QoE degradation in 5G network slice services due to complex and time-consuming root cause investigation processes, particularly in the transport network between the 5G cell site network function and the Edge/Core.
Innovation Solution
A system and method for detecting latency issues in 5G networks, collecting and analyzing network information to reengineer network slices, and deploying reengineered slices to meet quality requirements, utilizing machine learning and network management functions to autonomously manage network slice quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-centric root cause investigation processes are used to address QoS/QoE degradation, then comprehensive problem analysis is achieved, but the response time becomes excessively long (hours/days/weeks)
Solution Approach 1:
The system implements self-service through automated machine learning models that independently detect latency issues, analyze network information, determine affected network slices, and execute reengineering without human intervention. The closed-loop system continuously monitors QoS metrics and automatically remediate issues, eliminating the need for manual root cause investigation while maintaining high analysis accuracy through sophisticated algorithms.
Solution Approach 2:
The patent replaces the mechanical human-centric investigation process with an automated computational system using machine learning and artificial intelligence. The system substitutes human analysts with algorithms that can process network data, identify latency sources, and determine impacted network slices much faster than manual methods, reducing response time from hours/days to minutes or seconds.
2Productivity
If automated systems are implemented to reduce response time, then response speed improves, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by implementing a unified automated platform that performs multiple tasks: latency detection, network information collection, analysis, network slice identification, and reengineering deployment. This single system handles the entire QoS assurance workflow, reducing the need for multiple separate complex systems while improving overall productivity through integrated automation.
Solution Approach 2:
The patent introduces an intermediary layer of machine learning models and analytics functions that mediate between raw network data and automated decision-making. These intermediary components process and interpret network information, translating complex technical data into actionable insights that drive the reengineering process, thereby managing system complexity through structured intermediate processing layers.
3Loss of information
If manual root cause investigation is performed, then detailed problem understanding is achieved, but operational efficiency decreases
Solution Approach 1:
The system implements continuous feedback loops where QoS metrics are monitored, analyzed, and used to trigger automated responses. The feedback mechanism ensures that detailed problem understanding is maintained through comprehensive data collection and analysis, while operational efficiency is improved by using this information to automatically execute remediation actions without manual intervention, creating a closed-loop system that learns and adapts.
Solution Approach 2:
The patent applies preliminary action by pre-configuring the system with machine learning models, analysis frameworks, and reengineering templates before issues occur. When latency problems are detected, the system can immediately execute pre-planned remediation sequences, maintaining deep problem understanding through pre-established analysis protocols while dramatically improving operational efficiency by eliminating manual investigation and decision-making steps.
Data Source
AI summary
As described herein, a system, method, and computer program are provided for managing quality of 5G network slice services. A latency issue in a 5G network is detected. Information associated with the 5G network is collected. The information is analyzed to determine a network slice provisioned in the 5G network having a quality requirement that is not met as a result of the latency issue. The network slice is reengineered, using the information associated with the 5G network. The reengineered network slice is deployed in the 5G network.