A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network
Routing deployment and resource scheduling in communication networks require accurate traffic prediction. Neural network-based models that extract the time-correlated or space-correlated features of traffic flow have been developed for traffic prediction. The conventional model that extracts space-c...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10870254/ |
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| Summary: | Routing deployment and resource scheduling in communication networks require accurate traffic prediction. Neural network-based models that extract the time-correlated or space-correlated features of traffic flow have been developed for traffic prediction. The conventional model that extracts space-correlated features of traffic flow have the problem of high computational complexity and long training time which limits the model’s application on rapid routing deployment. This paper therefore proposes a layered training graph convolutional network (LT-GCN) to decrease the training time greatly with the nearly same prediction accuracy as graph convolutional network (GCN). Instead of training on parameters in all hidden layers simultaneously, LT-GCN develops a new layer-by-layer training pattern for multiple hidden layers to degrade the computational complexity in training process. LT-GCN is then further integrated with gated recurrent unit (GRU) that is called LTGG model to achieve the joint extraction of time-correlated and space-correlated features of traffic flow for more accurate prediction. Experimental results demonstrate that LT-GCN outperforms the classical GCN model on training time and LTGG exhibits greater performance than other benchmark models on prediction accuracy. |
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| ISSN: | 2169-3536 |