计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 296-300.doi: 10.11896/jsjkx.210400134

• 计算机网络 • 上一篇    下一篇

基于多通道稀疏LSTM的蜂窝流量预测研究

张争万1, 吴迪2, 张春炯2   

  1. 1 西南大学培训与继续教育学院 重庆400715
    2 同济大学电子与信息工程学院 上海201804
  • 收稿日期:2021-02-14 修回日期:2021-04-21 出版日期:2021-06-15 发布日期:2021-06-03
  • 通讯作者: 张春炯(chunjiongzhang@tongji.edu.cn)

Study of Cellular Traffic Prediction Based on Multi-channel Sparse LSTM

ZHANG Zheng-wan1, WU Di2, ZHANG Chun-jiong2   

  1. 1 College of Training and Continuing Education,Southwest University,Chongqing 400715,China
    2 College of Electronics and Information Engineering,Tongji University,Shanghai 201804,China
  • Received:2021-02-14 Revised:2021-04-21 Online:2021-06-15 Published:2021-06-03
  • About author:ZHANG Zheng-wan,born in 1978,master,engineer.His main research inte-rests include machine learning and wireless networks.(james129@swu.edu.cn)
    ZHANG Chun-jiong,born in 1990,Ph.D,engineer.His main research interests include federated learning and distributed robust optimization.

摘要: 下一代蜂窝网络在网络管理和服务供应场景中发挥着重要的作用,对移动网络流量的预测分析正变得越来越重要。文中针对城市蜂窝流量的预测,设计了一个基于多通道稀疏长期短期记忆网络(Long Short-Term Memory,LSTM)的流量预测模型。相对于多层感知器网络或其他神经网络结构,LSTM非常适合处理时间序列数据问题。所设计的多通道方式能够有效捕获多源网络流量信息,其稀疏方式使其自适应地对不同的流量时间节点赋予不同的权重,提高了深度神经网络模型捕捉重要特征的能力。在意大利米兰城市蜂窝流量数据上进行了实验,评估了所提方法对单步和多步预测的性能。实验结果展示出所提方法比基准方法更精准。此外,实验还报告了蜂窝流量中不同持续时间采样设置对LSTM网络模型的可存储长度及预测精度的影响。

关键词: LSTM, 蜂窝流量, 神经网络, 时间序列

Abstract: Next-generation cellular networks play an important role in network management and service provision,so that predictive analysis of mobile network traffic is becoming more and more important to our daily life.To predict the urban cellular traffic,this study designs a traffic prediction model based on multi-channel sparse LSTM.Compared with multilayer perceptron networks or other neural network structures,LSTM is very suitable for processing time series data.Therefore,the designed multi-channel method can effectively capture multi-source network traffic information,and its sparse method can adaptively assign different weights to different traffic time nodes,so that the ability of the deep neural network model to capture important features is improved.This study evaluates the performance of the proposed method with respect to the single-step and multi-step prediction using the cellular traffic data set in Milan,Italy.The experiment results show that the proposed method is more accurate than the benchmark methods.In addition,this study reports the impact of different sampling settings of cellular traffic on the storable length and accuracy of the LSTM network model.

Key words: Cellular traffic, LSTM, Neural networks, Time series

中图分类号: 

  • TP393
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