NWDAF Analytics for Edge Resource Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wireless communication systems, particularly in 5G networks, face challenges in optimizing edge computing resources and user equipment mobility, leading to inefficiencies in network operations and service quality.
Innovation Solution
The implementation of a Network Data Analytics Function (NWDAF) that collects and analyzes user equipment mobility information and edge application server data to provide predictive analytics for resource allocation and relocation, enabling more intelligent control of edge computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If edge computing resources are distributed across multiple locations to improve service quality, then service quality and network efficiency are improved, but resource allocation complexity and management difficulty increase
Solution Approach 1:
The NWDAF collects mobility information from UEs and EAS information from network functions, performs analytics to determine optimal resource allocation, and provides recommendations back to network functions for implementation. This closed-loop feedback mechanism enables intelligent resource allocation across distributed edge computing locations, resolving the complexity of managing multiple distributed resources while maintaining high service quality
Solution Approach 2:
The NWDAF acts as an intermediary between network functions that need analytics and the distributed edge computing resources. It receives requests from network functions, performs complex analytics on mobility and EAS information, and returns resource allocation recommendations. This intermediary approach simplifies the management complexity by centralizing the analytics function while enabling distributed resource optimization
2Productivity
If real-time analytics are performed to optimize resource allocation dynamically, then network efficiency and service quality improve, but processing time and computational overhead increase
Solution Approach 1:
The NWDAF performs analytics on mobility information and EAS information in advance to predict future resource requirements and generate allocation recommendations before they are critically needed. This preliminary analytics approach allows the system to proactively optimize resource allocation, reducing reactive processing time while maintaining high network efficiency through pre-computed insights
3Measurement precision
If comprehensive mobility information and EAS data are collected to improve analytics accuracy, then prediction accuracy and resource allocation quality improve, but data collection overhead and network traffic increase
Solution Approach 1:
The NWDAF extracts only the essential and relevant features from the comprehensive mobility information and EAS data that are critical for accurate predictions and resource allocation decisions. Rather than processing all raw data, it identifies and processes key parameters such as mobility patterns, location information, and application requirements. This extraction approach maintains high prediction accuracy while significantly reducing data collection overhead and energy consumption
Data Source
AI summary
Computer systems and methods are described for operating a network data and analytics function (NWDAF) in a cellular core network. The NWDAF receives user equipment (UE) mobility information from a first network function of the cellular network system and receives edge application server (EAS) information from a second network function of the cellular network system. The NWDAF derives data analytics based at least in part on the UE mobility information and the edge application server information. The NWDAF receives a request for the data analytics from a third network function in the cellular network system, and provides the data analytics to the third network function.