NWDAF Misbehaving UE Detection via Data Analysis
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Solution Overview
Problem
Current 5G network systems lack an efficient method to systematically identify misbehaving User Equipment (UEs), leading to increased troubleshooting workload and lack of automation in addressing UE-related issues.
Innovation Solution
The implementation of a Network Data Analytics Function (NWDAF) that receives equipment identifiers and rejection causes from network function nodes, analyzes this data to determine if a UE is misbehaving, and sends alerts to other network nodes for appropriate action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If case-by-case identification of misbehaving UEs is used, then troubleshooting can be performed, but the workload is significant and there is no connection among cases
Solution Approach 1:
The patent segments the troubleshooting process by categorizing UE behaviors into distinct types (misbehaving, normal, edge-case) based on analysis of rejection causes and back-off timer compliance. This segmentation enables systematic identification rather than ad-hoc case-by-case analysis, improving both reliability and productivity.
Solution Approach 2:
The patent implements preliminary action by proactively identifying and flagging misbehaving UEs before they cause network issues. The system continuously monitors UE behavior patterns, analyzes rejection causes, and pre-categorizes UEs as misbehving based on their compliance with back-off timers, enabling preventive rather than reactive troubleshooting.
2Productivity
If systematic identification of misbehving UEs is implemented, then troubleshooting workload is reduced and network automation is improved, but data analysis complexity increases
Solution Approach 1:
The patent applies universality by creating a multi-functional data analysis system that simultaneously performs multiple tasks: collecting rejection cause data, analyzing back-off timer compliance, categorizing UE behaviors, and generating actionable insights. This universal system handles diverse UE behaviors through a unified analytical framework, reducing overall system complexity despite the multifaceted analysis required.
Solution Approach 2:
The patent introduces an intermediary data analysis layer between raw network data and troubleshooting actions. This intermediary system processes rejection causes and timer compliance data, transforming complex raw data into simplified UE behavior categories that are easier to act upon, thereby reducing the complexity burden on both data collection and action execution systems.
3Extent of automation
If network automation is enhanced through data analysis, then operational efficiency improves, but the extent of automation requires sophisticated monitoring and analysis capabilities
Solution Approach 1:
The patent implements self-service by enabling the network system to automatically monitor, analyze, and identify misbehaving UEs without human intervention. The system autonomously collects data on rejection causes, analyzes back-off timer compliance, and categorizes UE behaviors, providing self-service monitoring that enhances automation while managing complexity through automated processes.
Solution Approach 2:
The patent incorporates feedback mechanisms where the data analysis system continuously monitors UE behavior, provides feedback on compliance with back-off timers, and adjusts its analysis based on observed patterns. This feedback loop enables sophisticated automation by allowing the system to learn from and adapt to UE behavior patterns, improving automation effectiveness while managing complexity through iterative refinement.
Data Source
AI summary
New methods are proposed to detect misbehaving UEs based on 5GS. The methods allow the network to react accurately and efficiently to deal with misbehaving UE(s).