计算机科学 ›› 2019, Vol. 46 ›› Issue (12): 108-113.doi: 10.11896/jsjkx.181102207
张杰1, 白光伟1, 沙鑫磊1, 赵文天1, 沈航1,2
ZHANG Jie1, BAI Guang-wei1, SHA Xin-lei1, ZHAO Wen-tian1, SHEN Hang1,2
摘要： 研究表明,历史流量数据可以用于移动网络流量的预测,同时周边区域的流量信息可以提高流量预测的准确性。为此,文中提出一种基于时空特征的移动网络流量预测模型STFM。STFM模型利用目标区域及周围区域的历史移动网络流量对目标区域的流量进行预测。其核心思想是,首先利用三维卷积网络(3D CNN)从流量中提取移动网络流量空间上的特征,再利用时间卷积网络(TCN)提取移动网络流量时间上的特征,最后全连接层对提取的特征与实际的流量值建立映射关系,产生预测的流量值。根据实验的验证与分析,STFM在移动网络流量预测上的标准均方根误差(NRMSE)相比TCN,CNN和CNN-LSTM分别减少了28%,21.7%和10%。因此,STFM模型能够有效提高移动网络流量预测的准确率。
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| [1]Cisco Visual Networking Index:Forecast and Methodology 2016－2021[EB/OL].https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vnicomplete-white-paper-c11-481360.html.[2]SUNDARESAN K.5G:An Evolution towards a Revolution[C]// Proceedings of ACM International Conference on Mobile Computing and Networking.New Delhi,India,2018:659-659.[3]IMRAN A,ZOHA A.Challenges in 5G:How to Empower SON with Big Data for Enabling 5G [J].IEEE Network,2014,28(6):27-33.[4]NIU Z,WU Y,GONG J,et al.Cell Zooming For Cost-Efficient Green Cellular Networks [J].IEEE Communications Magazine,2010,48(11):74-79.[5]XU F,LIN Y,HUANG J,et al.Big Data Driven Mobile Traffic Understanding and Forecasting:A Time Series Approach [J].IEEE Transactions on Services Computing,2016,9(5):796-805.[6]SHU Y,YU M,LIU J,et al.Wireless Traffic Modeling and Pre- diction Using Seasonal ARIMA Models[C]//Proceedings of IEEE International Conference on Communications.Anchorage:IEEE,2003:1675-1679.[7]LI R,ZHAO Z,ZHENG J,et al.The learning and prediction of application-level traffic data in cellular networks [J].IEEE Transactions on Wireless Communications,2017,16(6):3899-3912.[8]ZHANG C,PAUL P.Long-term mobile traffic forecasting using deep spatio-temporal neural networks[C]//Proceedings of ACM International Symposium on Mobile Ad Hoc Networking and Computing.Angeles:ACM,2018:231-240.[9]OLIVEIRA T P,BARBAR J S,SOARES A S.Computer Network Traffic Prediction:A Comparison between Traditional and Deep Learning Neural Networks [J].International Journal of Big Data Intelligence,2016,3(1):28-37.[10]NAREJO S,PASERO E.An Application of Internet Traffic Prediction with Deep Neural Network [J].Multidisciplinary Approaches to Neural Computing,2018,69(1):139-149.[11]HUANG C W,CHIANG C T,LI Q.A Study of Deep Learning Networks on Mobile Traffic Forecasting[C]//Proceedings of IEEE Personal,Indoor,and Mobile Radio Communications.Montreal:IEEE,2017:1-6.[12]LI R,ZHAO Z,ZHOU X,et al.The Prediction Analysis of Cellular Radio Access Network Traffic:From Entropy Theory to Networking Practice [J].IEEE Communications Magazine,2014,52(6):234-240.[13]FU R,ZHANG Z,LI L.Using LSTM and GRU neural network methods for traffic flow prediction[C]//Proceedings of IEEE Youth Academic Annual Conference of Chinese Association of Automation.Wuhan:IEEE Press,2016:324-328.[14]YU F,KOLTUN V.Multi-scale context aggregation by dilated convolutions[C]//Proceedings of International Conference on Learning Representations.San Juan:ICLR,2016.[15]HE K,ZHANG X,REN S,et al.Deep residual learning for ima- ge recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Nevada:IEEE,2016:770-778.[16]ZHOU F Y,JIN L P,DONG J.Review of Convolutional Neural Network[J].Chinese Journal of Computers,2017,40(6):1229-1251.[17]IOFFE S,SZEGEDY C.Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceedings of International Conference on International Conference on Machine Learning.Lille:ACM,2015:448-456.[18]GIANNI B,MARCO D N,ROBERTO L,et al.A multi-source dataset of urban life in the city of Milan and the Province of Trentino[J].Scientific Data,2015,2:150055. |
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