A Model Drift Detection and Adaptation Framework for 5G Core Networks

TL;DR

This paper presents a framework for detecting and adapting to model drift in 5G core networks, enhancing the reliability of AI-driven network management amidst dynamic user behaviors.

Contribution

It introduces a novel drift detection and adaptation framework specifically designed for 5G core networks, validated through a functional prototype.

Findings

Accurately detects model drift in 5G network scenarios.

Effectively initiates remediation to restore system performance.

Demonstrates framework's applicability in real network conditions.

Abstract

The advent of Fifth Generation (5G) and beyond 5G networks (5G+) has revolutionized the way network operators consider the management and orchestration of their networks. With an increased focus on intelligence and automation through core network functions such as the NWDAF, service providers are tasked with integrating machine learning models and artificial intelligence systems into their existing network operation practices. Due to the dynamic nature of next-generation networks and their supported use cases and applications, model drift is a serious concern, which can deteriorate the performance of intelligent models deployed throughout the network. The work presented in this paper introduces a model drift detection and adaptation module for 5G core networks. Using a functional prototype of a 5G core network, a drift in user behaviour is emulated, and the proposed framework is deployed and tested. The results of this work demonstrate the ability of the drift detection module to accurately characterize a drifted concept as well as the ability of the drift adaptation module to begin the necessary remediation efforts to restore system performance.