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Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network

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Abstract

This paper addresses the challenges of exponentially growing traffic in cellular networks by proposing a novel predictive model, Hybrid Graph Convolutional Recurrent Network (HGCRN), which combines static graph convolutional recurrent neural network and meta-graph learning. The model is designed to effectively capture the complex spatio-temporal dependencies in network traffic, enhancing prediction accuracy and operational efficiency. By constructing graph adjacency matrices that go beyond mere geographical proximity, HGCRN offers a deeper understanding of the dynamic interactions within the network. Tested on real-world datasets from Telecom Italia and China Mobile, the model demonstrates significant improvements over traditional and state-of-the-art methods in terms of predictive accuracy and reliability. HGCRN outperforms other models in terms of MAE, MAPE, and RMSE, specifically, our model achieves a lower RMSE of 6.53 compared to the state-of-the-art on a publicly available Telecom dataset, and an RMSE of 11.79, outperforming existing methods in the step-12 setting on the China Mobile dataset.

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Data Availibility

The datasets analyzed during the current study are available in https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EGZHFV and https://jiutian.10086.cn/open/#/dataset/710002?platform=OpenInnovation.

References

  1. Ericsson: Mobile data traffic outlook. Technical report (2023).

  2. Ericsson: Resilient 5g uptake: Global mobile data traffic set to triple in six years. Technical report (2023). https://www.ericsson.com/en/reports-and-papers/mobility-report/reports/november-2023.

  3. Ranjha, A., Naboulsi, D., & El-Emary, M. (2022). Towards facilitating urllc in uav-enabled mec systems for 6g networks. In: International Symposium on Ubiquitous Networking, pp. 55–67. Springer.

  4. Ranjha, A., Naboulsi, D., El Emary, M., & Gagnon, F. (2024). Facilitating urllc vis-á-vis uav-enabled relaying for mec systems in 6-g networks. IEEE Transactions on Reliability.

  5. Ranjha, A., Javed, M.A., Piran, M.J., Asif, M., Hussien, M., Zeadally, S., & Frnda, J. (2023). Towards facilitating power efficient urllc systems in uav networks under jittering. IEEE Transactions on Consumer Electronics.

  6. Barlacchi, G., De Nadai, M., Larcher, R., Casella, A., Chitic, C., Torrisi, G., Antonelli, F., Vespignani, A., Pentland, A., & Lepri, B. (2015). A multi-source dataset of urban life in the city of milan and the province of trentino. Scientific data, 2(1), 1–15.

    Article  Google Scholar 

  7. Mobile, C. (2023). Wireless Cells Network Multi-Index Spatio-Temporal Prediction. China Mobile. https://doi.org/10.12448/3s6s-w713

  8. Zhao, S., Jiang, X., Jacobson, G., Jana, R., Hsu, W.-L., Rustamov, R., Talasila, M., Aftab, S.A., Chen, Y., & Borcea, C. (2020). Cellular network traffic prediction incorporating handover: A graph convolutional approach. In: 2020 17th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pp. 1–9. IEEE.

  9. Yu, L., Li, M., Jin, W., Guo, Y., Wang, Q., Yan, F., & Li, P. (2020). Step: A spatio-temporal fine-granular user traffic prediction system for cellular networks. IEEE Transactions on Mobile Computing, 20(12), 3453–3466.

    Article  Google Scholar 

  10. Zhao, N., Wu, A., Pei, Y., Liang, Y.-C., & Niyato, D. (2021). Spatial-temporal aggregation graph convolution network for efficient mobile cellular traffic prediction. IEEE Communications Letters, 26(3), 587–591.

    Article  Google Scholar 

  11. Jiang, W., Zhang, Y., Han, H., Huang, Z., Li, Q., & Mu, J. (2024). Mobile traffic prediction in consumer applications: A multimodal deep learning approach. IEEE Transactions on Consumer Electronics., 70(1), 3425–3435.

    Article  Google Scholar 

  12. Liu, S., He, M., Wu, Z., Lu, P., & Gu, W. (2024). Spatial-temporal graph neural network traffic prediction based load balancing with reinforcement learning in cellular networks. Information Fusion, 103, 102079.

    Article  Google Scholar 

  13. Wang, Z., Hu, J., Min, G., Zhao, Z., Chang, Z., & Wang, Z. (2022). Spatial-temporal cellular traffic prediction for 5g and beyond: A graph neural networks-based approach. IEEE Transactions on Industrial Informatics, 19(4), 5722–5731.

    Article  Google Scholar 

  14. Yao, Y., Gu, B., Su, Z., & Guizani, M. (2021). Mvstgn: A multi-view spatial-temporal graph network for cellular traffic prediction. IEEE Transactions on Mobile Computing, 22(5), 2837–2849.

    Article  Google Scholar 

  15. Chen, X., Chuai, G., Zhang, K., & Gao, W. (2023). Spatial-temporal cellular traffic prediction: A novel method based on causality and graph attention network. In: 2023 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE.

  16. Müller, M. (2007). Dynamic time warping. Information retrieval for music and motion, 69–84.

  17. Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: International Conference on Learning Representations.

  18. Yu, B., Yin, H., & Zhu, Z. (2018). Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 3634–3640.

  19. Liu, Z., & Zhou, J. (2020). Introduction to graph neural networks. Synthesis Lectures on Artificial Intelligence and Machine Learning.

  20. Bai, L., Yao, L., Li, C., Wang, X., & Wang, C. (2020). Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems, 33, 17804–17815.

    Google Scholar 

  21. Shang, C., Chen, J., & Bi, J. (2021). Discrete graph structure learning for forecasting multiple time series. In: International Conference on Learning Representations

  22. Zeng, S., Huang, X., & Lin, D. (2023). Fair computation offloading for a multi-antenna noma aided mobile edge computing network. Acta Electronica Sinica.

  23. Ye, J., Sun, L., Du, B., Fu, Y., & Xiong, H. (2021). Coupled layer-wise graph convolution for transportation demand prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4617–4625.

  24. Man, J., Dong, H., Yang, X., Meng, Z., Jia, L., Qin, Y., & Xin, G. (2022). Gcg: Graph convolutional network and gated recurrent unit method for high-speed train axle temperature forecasting. Mechanical Systems and Signal Processing, 163, 108102.

    Article  Google Scholar 

  25. Kaur, G., Grewal, S. K., & Jain, A. (2024). Federated learning based spatio-temporal framework for real-time traffic prediction. Wireless Personal Communications, 136(2), 849–65.

    Article  Google Scholar 

  26. Thakur, P., Kansal, V., & Rishiwal, V. (2024). Hybrid deep learning approach based on lstm and cnn for malware detection. Wireless Personal Communications, 136(3), 1879–901.

    Article  Google Scholar 

  27. Yao, H., Liu, Y., Wei, Y., Tang, X., & Li, Z. (2019). Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In: The World Wide Web Conference, pp. 2181–2191.

  28. Pan, Z., Liang, Y., Wang, W., Yu, Y., Zheng, Y., & Zhang, J. (2019). Urban traffic prediction from spatio-temporal data using deep meta learning. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1720–1730.

  29. Park, H., Noh, J., & Ham, B. (2020). Learning memory-guided normality for anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14372–14381.

  30. Pan, Z., Zhang, W., Liang, Y., Zhang, W., Yu, Y., Zhang, J., & Zheng, Y. (2022). Spatio-temporal meta learning for urban traffic prediction. IEEE Transactions on Knowledge & Data Engineering, 34(03), 1462–1476.

    Article  Google Scholar 

  31. Jiang, R., Wang, Z., Yong, J., Jeph, P., Chen, Q., Kobayashi, Y., Song, X., Fukushima, S., & Suzumura, T. (2023). Spatio-temporal meta-graph learning for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 8078–8086.

  32. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.

    Article  Google Scholar 

  33. Cho, K., Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using rnn encoder–decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734.

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Funding

This work was supported by the National Natural Science Foundation of China under the Grant no. 62371057; and the 111 Project of China under the Grant no. B08004.

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Authors and Affiliations

  1. Artificial Intelligence, Beijing University of Posts and Telecommunications, Xitucheng Road, Beijing, 100876, China

    Miaoru Zhang, Hao Zhou, Ke Yu & Xiaofei Wu

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  1. Miaoru Zhang
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  2. Hao Zhou
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  3. Ke Yu
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  4. Xiaofei Wu
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Contributions

M.Z. wrote the main manuscript text and H.Z., K.Y., and X.W made contributions to the conception. All authors reviewed the manuscript.

Corresponding author

Correspondence to Ke Yu.

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Zhang, M., Zhou, H., Yu, K. et al. Cellular Network Traffic Prediction with Hybrid Graph Convolutional Recurrent Network. Wireless Pers Commun 138, 1867–1892 (2024). https://doi.org/10.1007/s11277-024-11580-8

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