NWDAF Mobility Prediction for UE Service Continuity Across PLMN and SNPN
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Solution Overview
Problem
Existing systems face challenges in providing seamless service continuity for user equipment (UE) when moving between stand-alone non-public networks (SNPN) and public land mobile networks (PLMN), particularly due to difficulties in detecting coverage loss and managing handovers or user plane switching efficiently.
Innovation Solution
A method involving a 5G network data analytics function (NWDAF) is used to predict UE mobility and provide analysis information for instructing handovers or user plane resource switching between non-3GPP and 3GPP access, ensuring service continuity by activating or deactivating user plane resources as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If UE moves between SNPN and PLMN using non-3GPP access, then service continuity can be maintained, but coverage loss detection becomes difficult and handover management becomes complex
Solution Approach 1:
The NWDAF performs preliminary analysis of UE mobility patterns and predicts future network transitions before they occur. The system proactively identifies when a UE is likely to move between networks and triggers handover procedures in advance, preventing service disruption while simplifying the detection of coverage loss by using predictive analytics rather than reactive monitoring.
2Reliability
If the system monitors UE mobility in real-time, then service continuity can be ensured, but network resources are consumed and latency increases
Solution Approach 1:
The NWDAF analyzes UE mobility patterns and predicts future network transitions before they occur. By using historical data and mobility models, the system proactively identifies when a UE is likely to move between networks, triggering handover procedures in advance rather than reacting to real-time coverage changes, thus reducing detection time and latency.
Solution Approach 2:
The system continuously collects UE mobility data and feedback from network nodes, feeds this information back to the NWDAF for analysis, and uses the insights to refine future predictions. This feedback loop enables the system to improve its accuracy over time while maintaining efficient resource utilization, as the feedback mechanism operates asynchronously rather than requiring continuous real-time processing.
3Reliability
If the system activates multiple user plane resources for handover, then service continuity is improved, but network complexity and resource consumption increase
Solution Approach 1:
The NWDAF predicts when a UE is likely to move between networks and triggers handover procedures in advance. By preparing the target network resources beforehand based on predictive analytics, the system can activate user plane resources more efficiently, only when and where needed, rather than maintaining multiple redundant resources continuously, thus improving service continuity while reducing overall resource consumption.
Data Source
AI summary
Provided is a method for supporting service continuity for UE, moving between a PLMN and an SNPN, by means of the steps of: receiving analysis information relating to UE, which is configured with a PDU session with an SNPN, from the NWDAF of a PLMN by means of non-3GPP access; determining that the UE will move from the PLMN to the SNPN on the basis of the analysis information; and instructing the UE to change the access for the SNPN PDU session from non-3GPP access to 3GPP access, on the basis of the determination.