NWDAF Mobility Analytics Using eLCS Location Data
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Solution Overview
Problem
The Network Data Analytics Function (NWDAF) in 3GPP Release 17 is unable to retrieve location data using enhanced Location Services (eLCS), limiting its capability to derive accurate analytics for User Equipment (UE) mobility events, particularly for UEs in motion.
Innovation Solution
The NWDAF is enhanced to collect location data using eLCS services, enabling it to derive analytics for UEs in motion by identifying and tracking their mobility, direction, and presence in specific zones, thereby providing more accurate mobility analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NWDAF collects data from Network Functions as defined in 3GPP Release 17, then basic mobility analytics can be derived, but the capability to retrieve location data using eLCS services is not available, limiting accuracy for UEs in motion
Solution Approach 1:
The patent introduces dynamic filtering capabilities that allow the NWDAF to adaptively select and process location data based on movement thresholds and time periods. The system dynamically adjusts which data points to collect and analyze based on UE motion characteristics, enabling accurate tracking of mobile devices while maintaining flexibility for different analytics scenarios.
Solution Approach 2:
The patent changes the parameters of location data collection by introducing movement-based filtering criteria (minimum distance, time period) and selective data retrieval. This transforms the static data collection approach into a dynamic parameter-driven system that adapts to UE mobility patterns, improving location accuracy for moving devices.
2Measurement precision
If NWDAF retrieves detailed location data for mobility analytics, then accuracy for UEs in motion improves, but data collection complexity and processing requirements increase
Solution Approach 1:
The patent extracts only the necessary location data points that meet specific movement criteria from the complete set of available location information. By filtering out redundant data and retaining only relevant mobility events based on distance and time thresholds, the system reduces data collection complexity while maintaining analytical accuracy.
Solution Approach 2:
The patent applies partial action by selectively collecting location data only when movement thresholds are exceeded, rather than continuously collecting all location information. This approach gathers sufficient data for accurate mobility analytics while minimizing the overall data volume and processing requirements.
Data Source
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AI summary
Apparatuses, methods, and systems are disclosed for deriving analytics for UE mobility events. One apparatus (700) includes a transceiver (725) that receives (805) a first request for analytics, where the first request indicates a first set of requirements for UE mobility including a minimum distance and a specific time period requirement and a processor (705) that identifies (810) a set of UEs for which to retrieve location data for mobility events based on the first set of requirements. The processor (705) retrieves (815) location data for the identified set of UEs for mobility events during the specific time period that satisfy the minimum distance and time period and derives (820) analytics based on the first request.