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With the growth of the telecommunication industry and the popularization of mobile devices, the demand for mobile data traffic continues to increase, leading to a rising load on wireless networks. Anticipating business needs and allocation of wireless network resources has received increasing attention. Performance Management (PM) data provides statistics on various performance indicators of wireless network, including current data and historical data, such as the peak value of user traffic within a day, etc. An important issue is how to precisely establish a wireless network traffic prediction model for base station and timely forecast their capacity expansion needs. This paper establishes prediction models for the downlink PDCP layer user traffic of a single base station based on Transformer and Long Short-Term Memory (LSTM) neural network separately. The future traffic of base station can be effectively predicted, providing precise data support for intelligent expansion of wireless networks.

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Research on Wireless Network Traffic Prediction Based on Deep Learning and Performance Management Data

  • 会议论文

  • 发表日期：

    2025年

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  • Haixin Li,
  • Zuxin Yin,
  • Xiaodong Wang,
  • Yu Wang,
  • Jiaojiao Zhang,
  • Yi Wang,
  • Man Zhang,
  • Ruihong An,
  • Yuhui Han,
  • Dong Chen

摘要

With the growth of the telecommunication industry and the popularization of mobile devices, the demand for mobile data traffic continues to increase, leading to a rising load on wireless networks. Anticipating business needs and allocation of wireless network resources has received increasing attention. Performance Management (PM) data provides statistics on various performance indicators of wireless network, including current data and historical data, such as the peak value of user traffic within a day, etc. An important issue is how to precisely establish a wireless network traffic prediction model for base station and timely forecast their capacity expansion needs. This paper establishes prediction models for the downlink PDCP layer user traffic of a single base station based on Transformer and Long Short-Term Memory (LSTM) neural network separately. The future traffic of base station can be effectively predicted, providing precise data support for intelligent expansion of wireless networks.