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Large-scale cellular traffic prediction based on graph convolutional networks with transfer learning

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Abstract

Intelligent cellular traffic prediction is very important for mobile operators to achieve resource scheduling and allocation. In reality, people often need to predict very large scale of cellular traffic involving thousands of cells. This paper proposes a transfer learning strategy based on graph convolution neural network to achieve the task of large-scale traffic prediction. In this paper, we design a novel spatial-temporal graph convolutional network based on attention mechanism (STA-GCN). In order to achieve large-scale traffic prediction, this paper proposes a regional transfer learning strategy based on STA-GCN to improve knowledge reuse. The effectiveness of STA-GCN is validated through two real-world traffic datasets. The results show that STA-GCN outperforms the state-of-art baselines, and the transfer learning strategy can effectively reduce the number of epochs while training.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No.61971057 and MoE-CMCC ”Artifical Intelligence” Project No. MCM20190701.

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Authors and Affiliations

  1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xu Zhou, Yong Zhang, Zhao Li & Zhao Zhang

  2. Beijing Key Laboratory of Work Safety Intelligent Monitoring, Beijing University of Posts and Telecommunications, Beijing, China

    Yong Zhang

  3. Center of AI and Intelligent Operation R&D, China Mobile Research Institude, Beijing, China

    Xing Wang & Juan Zhao

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  1. Xu Zhou
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  2. Yong Zhang
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  3. Zhao Li
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  4. Xing Wang
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  5. Juan Zhao
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Correspondence to Yong Zhang.

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Zhou, X., Zhang, Y., Li, Z. et al. Large-scale cellular traffic prediction based on graph convolutional networks with transfer learning. Neural Comput & Applic 34, 5549–5559 (2022). https://doi.org/10.1007/s00521-021-06708-x

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