NWDAF Proximity Analytics Using Multi-Source UE Data
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Solution Overview
Problem
Current 5G communication systems lack efficient methods for providing relative proximity information of User Equipments (UEs) in a network, which is essential for accurate analytics and load estimation in 5G mobile communication systems.
Innovation Solution
A method and system utilizing a Network Data Analytics Function (NWDAF) that collects and combines trajectory data, Minimization of Drive Tests (MDT) data, and collective behavior data of UEs to estimate Network Function (NF) load and provide relative proximity analytics, using input from various network entities like OAM, AF, and DCAF.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources (trajectory data, MDT data, collective behavior data) are collected and combined for network analytics, then the accuracy of NF load estimation and relative proximity analytics is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The network entity is divided into specialized functional modules: a data collection module that gathers trajectory data, MDT data, and collective behavior data from multiple sources; a data processing module that cleanses and validates the collected data; and an analytics generation module that produces NF load estimation and relative proximity analytics. This segmentation allows each module to handle specific tasks efficiently, improving measurement precision while managing device complexity through functional decomposition.
Solution Approach 2:
The network entity acts as an intermediary between multiple data sources (UEs, OAM, AF, DCAF) and the analytics consumers. It collects data from diverse sources, processes it through standardized procedures, and provides unified analytics outputs. This intermediary role enables accurate NF load estimation by harmonizing data from multiple sources without requiring direct complex interactions between all system components.
2Measurement precision
If multiple data sources (trajectory data, MDT data, collective behavior data) are collected and combined for network analytics, then the accuracy of relative proximity analytics is improved, but the loss of time for data collection and processing increases
Solution Approach 1:
The system performs preliminary data collection and storage of trajectory data, MDT data, and collective behavior data in data repositories before analytics are requested. This allows the network entity to have pre-processed data available when analytics requests arrive, reducing the time required for actual analytics generation while maintaining high accuracy through the use of pre-collected multi-source data.
Solution Approach 2:
The network entity continuously collects and processes data from multiple sources, maintaining an ongoing stream of updated information about UE locations, behaviors, and network conditions. This continuous data collection ensures that when analytics requests are made, the most current and accurate data is available, improving relative proximity analytics accuracy without requiring time-consuming on-demand data gathering.
Data Source
AI summary
The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method performed by a first network entity in a mobile communication system for providing information relating to the relative proximity of one or more user equipments (UEs) is provided. The method includes receiving, from a second network entity, a request for network analytics; receiving, from one or more third network entities, input data relating to the UEs; generating analytics based on the input data; and providing the generated analytics to the second network entity as a response to the request. The request comprises an analytics identifier (ID) indicating relative proximity information.