Traffic Prediction in 3G Mobile Networks Based on Multifractal Exploration  

作　　者：Yanhua Yu Meina Song Yu Fu Junde Song 

机构地区：[1]the School of Computer,Beijing University of Posts and Telecommunications

出　　处：《Tsinghua Science and Technology》2013年第4期398-405,共8页清华大学学报（自然科学版（英文版）

基　　金：the National Key project of Scientific and Technical Supporting Programs of China (No. 2009BAH39B03);the National Natural Science Foundation of China (No. 61072060);the National High-Tech Research and Development (863) Program of China (No. 2011AA100706);the Program for New Century Excellent Talents in University (No. NECET-08-0738);the Research Fund for the Doctoral Program of Higher Education (No. 20110005120007);the Co-construction Program with Beijing Municipal Commission of Education;Engineering Research Center of Information Networks, Ministry of Education

摘　　要：Traffic prediction plays an integral role in telecommunication network planning and network optimization. In this paper, we investigate the traffic forecasting for data services in 3G mobile networks. Although the Box-Jenkins model has been proven to be appropriate for voice traffic （since the arrival of calls follows a Poisson distribution）, it has been demonstrated that the Internet traffic exhibits statistical self-similarity and has to be modeled using the Fractional AutoRegressive Integrated Moving Average （FARIMA） process. However, a few studies have concluded that the FARIMA process may fail in modeling the Internet traffic. To this end, we conducted experiments on the modeling of benchmark Internet traffic and found that the FARIMA process fails because of the significant multifractal characteristic inherent in the traffic series. Thereafter, we investigate the traffic series of data services in a 3G mobile network from a province in China. Rich multifractal spectra are found in this series. Based on this observation, an integrated method combining the AutoRegressive Moving Average （ARMA） and FARIMA processes is applied. The obtained experimental results verify the effectiveness of the integrated prediction method.Traffic prediction plays an integral role in telecommunication network planning and network optimization. In this paper, we investigate the traffic forecasting for data services in 3G mobile networks. Although the Box-Jenkins model has been proven to be appropriate for voice traffic （since the arrival of calls follows a Poisson distribution）, it has been demonstrated that the Internet traffic exhibits statistical self-similarity and has to be modeled using the Fractional AutoRegressive Integrated Moving Average （FARIMA） process. However, a few studies have concluded that the FARIMA process may fail in modeling the Internet traffic. To this end, we conducted experiments on the modeling of benchmark Internet traffic and found that the FARIMA process fails because of the significant multifractal characteristic inherent in the traffic series. Thereafter, we investigate the traffic series of data services in a 3G mobile network from a province in China. Rich multifractal spectra are found in this series. Based on this observation, an integrated method combining the AutoRegressive Moving Average （ARMA） and FARIMA processes is applied. The obtained experimental results verify the effectiveness of the integrated prediction method.

关 键 词：time series prediction self-similar Fractional AutoRegressive Integrated Moving Average （FARIMA） 

分 类 号：TN929.5[电子电信—通信与信息系统] U491.113[电子电信—信息与通信工程]