Beyond 5G Network Failure Classification for Network Digital Twin Using Graph Neural Network
This work addresses failure detection in complex 5G and beyond networks for network operators, but it appears incremental as it combines existing techniques (GFT and MPNN) for a specific domain application.
The paper tackles the problem of classifying rare failure types in 5G and beyond networks using network digital twins, which involves imbalanced multiclass classification, and proposes a method integrating graph Fourier transform with a message-passing neural network, achieving accurate failure classification in real and simulated environments.
Fifth-generation (5G) core networks in network digital twins (NDTs) are complex systems with numerous components, generating considerable data. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classes in multiclass classification. To address this problem, we propose a novel method of integrating a graph Fourier transform (GFT) into a message-passing neural network (MPNN) designed for NDTs. This approach transforms the data into a graph using the GFT to address class imbalance, whereas the MPNN extracts features and models dependencies between network components. This combined approach identifies failure types in real and simulated NDT environments, demonstrating its potential for accurate failure classification in 5G and beyond (B5G) networks. Moreover, the MPNN is adept at learning complex local structures among neighbors in an end-to-end setting. Extensive experiments have demonstrated that the proposed approach can identify failure types in three multiclass domain datasets at multiple failure points in real networks and NDT environments. The results demonstrate that the proposed GFT-MPNN can accurately classify network failures in B5G networks, especially when employed within NDTs to detect failure types.