Fault diagnosis of 5G networks based on digital twin model

Abstract

Fault diagnosis of 5G networks faces the challenges of heavy reliance on human experience and insufficient fault samples and relevant monitoring data. The digital twin technology can realize the interaction between virtual space and physical space through the fusion of model and data, providing a new paradigm for fault diagnosis. In this paper, we first propose a network digital twin model and apply it to 5G network diagnosis. We then use an improved Average Wasserstein GAN with Gradient Penalty (AWGAN-GP) method to discover and predict failures in the twin network. Finally, we use XGBoost algorithm to locate the faults in physical network in real time. Extensive simulation results show that the proposed approach can significantly increase fault prediction and diagnosis accuracy in the case of a small number of labeled failure samples in 5G networks.