Transnet: A High-accuracy Network Delay Prediction Model via Transformer and GNN in 6G
Shengyi Ding, Jin Li, Yonghan Wu, Danshi Wang, Min Zhang · 2024
In future 6G, bounded delay, ultra-high-reliability, and dedicated services will require high-performance network modeling techniques for the accuracy pre-validation such as network delay prediction. Recently, graph neural networks (GNNs) have been shown great potential for network delay prediction. GNNs are able to effectively capture complex topologies and node features in graph data by recursively aggregating the neighborhood information of nodes. To improve the ability to learn representations of graph data, GNNs are suitable for a variety of complex network modeling tasks with high flexibility and powerful scalability. However, the current GNN-based Routenet model can not capture the effect of the path on neighboring links, which is ineffective in complex topologies. To model the effects of the network path on neighboring links, this paper proposes a transformer-based GNN model named Transnet. In this model, the Transformer is first introduced to update the path and link states and describe the effects of the path on neighboring links based on the attention mechanism in the Transformer. Simulation results show that the delay prediction accuracy of the proposed Transnet obviously exceeds those of Routenet on multi-node topology in the Nsfnet and Synth50 datasets.