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A stacked broad learning system with multitask learning method for cellular wireless network traffic prediction

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Abstract

With the development of 5G networks, cellular wireless networks are becoming more diverse and intelligent. As an important part of intelligent network management, wireless network traffic prediction has attracted more attention. Meanwhile, low delay communication is also an important part of the prediction task that needs to be considered. However, traditional deep learning models have many drawbacks in traffic prediction, such as excessive running time and computing resources. To address these issues, especially jointly considering effectiveness and efficiency, we propose a stacked broad learning system with multitask learning method for traffic flow prediction, called MTL-SBLS. Specifically, we use related tasks with similar change patterns as input to the prediction model and share more relevant features through multi-task learning. The multi-layer stacking structure of stacking broad learning system can effectively capture the traffic data features and ensure high prediction performance. When stacking new blocks, the fixed structure and weight of the underlying basic broad learning system blocks ensure that the newly generated stacking broad learning system still has a low computational cost. Finally, the experiments on three real data sets demonstrate that the MTL-SBLS model outperforms the other existing prediction methods (93.38% prediction accuracy on average). Furthermore, the MTL-SBLS model can maintain a running time of less than 10 sec on all three datasets, indicating that it is efficient. Thus, the MTL-SBLS model is proved to improve the accuracy of traffic flow prediction while maintaining low complexity and running time.

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Data availability statement

The datasets analyzed during this study are available in by Telecom Italia. The MathorCup database are available in 2018 MathorCup Modeling Challenge database.

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Acknowledgements

The work described in this paper is supported by the National Natural Science Foundation of China (No.U1936122), the Primary Research & Development Plan of Hubei Province (No.2020BA B101), and the Primary Research & Development Plan of Hubei Province (No.2020BAA003).

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Wuhan University, Wuhan, 430000, China

    Lingzi Zhu & Bo Zhao

  2. School of Computer Science, Wuhan University, Wuhan, 430000, China

    Yang An

Authors
  1. Lingzi Zhu
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  2. Bo Zhao
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  3. Yang An
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Correspondence to Bo Zhao.

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Communicated by Oscar Castillo.

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Zhu, L., Zhao, B. & An, Y. A stacked broad learning system with multitask learning method for cellular wireless network traffic prediction. Soft Comput 27, 13445–13460 (2023). https://doi.org/10.1007/s00500-022-07718-0

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