NWDAF Signaling Storm Analytics for 5G Network Stability
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Solution Overview
Problem
Existing 5G mobile communication systems lack effective mechanisms for predicting, detecting, preventing, and mitigating abnormal network behaviors such as signaling storms, which can lead to service degradation and interruptions due to cyber-attacks, NF overloading, and unexpected failures.
Innovation Solution
Implementing a Network Data Analytics Function (NWDAF) to assist in predicting, detecting, preventing, and mitigating abnormal network behaviors by collecting data from various network functions, generating analytics, and providing output to consumer network functions for proactive management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional 5G network operations are used without NWDAF, then the network structure remains simple, but the network cannot effectively predict or detect abnormal behaviors such as signaling storms
Solution Approach 1:
The NWDAF performs preliminary actions by collecting data from multiple network functions and generating analytics predictions before abnormal behaviors occur. This allows the network to proactively identify potential signaling storms and take preventive measures, improving reliability through early detection while adding the complexity of predictive analytics functions.
Solution Approach 2:
The NWDAF acts as an intermediary between consumer network functions and producer network functions. It collects data from producers, processes it through analytics, and provides insights to consumers, thereby enabling abnormal behavior detection without requiring direct complex interactions between all network functions, thus managing complexity through a centralized intermediary layer.
2Loss of time
If real-time analytics processing is implemented to detect abnormal behaviors, then detection speed improves, but processing time and computational resources increase
Solution Approach 1:
The NWDAF continuously collects and pre-processes data from network functions in real-time, maintaining updated analytics models before abnormalities occur. This preliminary continuous processing enables rapid detection when anomalies arise without requiring intensive computational resources at the moment of detection, thus reducing detection time while managing energy consumption through distributed continuous monitoring.
3Measurement precision
If comprehensive data collection from multiple network functions is performed, then detection accuracy improves, but data processing complexity increases
Solution Approach 1:
The NWDAF segments data collection and processing by interacting with specific producer network functions (AMF, SMF, UPF, PCF, NRF) that have well-defined interfaces. Each network function provides specific types of data relevant to its operation, allowing the NWDAF to collect comprehensive data in a structured, modular manner. This segmentation improves detection accuracy through comprehensive data while managing processing complexity through standardized interfaces and distributed data collection.
Data Source
AI summary
The disclosure relates to a fifth generation (5G) or sixth generation (6G) communication system for supporting a higher data transmission rate. A method and device are provided in which a network data analytics function (NWDAF) receives, from a consumer network function, at least one of a subscription to assistance information for signaling storm analytics or a request for the assistance information. In response to receiving the at least one of the subscription or the request, the NWDAF collects input data from at least one network function. The NWDAF generates signaling storm output analytics based on the input data. The signaling storm analytics include a signaling storm cause. The NWDAF sends the signaling storm output analytics to the consumer network function.