Deep Learning-Based Modeling of 5G Core Control Plane for 5G Network Digital Twin

TL;DR

This paper introduces two deep learning architectures, 5GC-Seq2Seq and 5GC-former, to model the 5G core network control plane, demonstrating high accuracy and robustness in replicating core functionalities using real signaling data.

Contribution

The paper presents novel data-driven deep learning models for 5G control plane modeling, including a Transformer-based architecture, and provides a dataset and evaluation demonstrating their effectiveness.

Findings

5GC-Seq2Seq achieves 99.997% F1-score in simple scenarios.

5GC-former surpasses 99.999% F1-score and handles concurrency effectively.

Models accurately replicate key 5G core network control plane functions.

Abstract

Digital twin serves as a crucial facilitator in the advancement and implementation of emerging technologies within 5G and beyond networks. However, the intricate structure and diverse functionalities of the existing 5G core network, especially the control plane, present challenges in constructing core network digital twins. In this paper, we propose two novel data-driven architectures for modeling the 5G control plane and implement corresponding deep learning models, namely 5GC-Seq2Seq and 5GC-former, based on the Vanilla Seq2Seq model and Transformer decoder respectively. We also present a solution enabling the interconversion of signaling messages and length-limited vectors to construct a dataset. The experiments are based on 5G core network signaling messages collected by the Spirent C50 network tester, encompassing various procedures such as registration, handover, and PDU sessions. The results show that 5GC-Seq2Seq achieves a 99.997\% F1-score (a metric measuring the accuracy of positive samples) in single UE scenarios with a simple structure, but exhibits significantly reduced performance in handling concurrency. In contrast, 5GC-former surpasses 99.999\% F1-score while maintaining robust performance under concurrent UE scenarios by constructing a more complex and highly parallel model. These findings validate that our method accurately replicates the principal functionalities of the 5G core network control plane.