Dissertation
Dissertation > Industrial Technology > Radio electronics, telecommunications technology > Wireless communications

The Research of Traffic Analysis and Forecasting Method for Wireless Network

Author ZhangSheng
Tutor LiuXingWei
School West China University
Course Applied Computer Technology
Keywords Wireless Network Data Analysis Flow projections Grey Theory Chaos Theory Support Vector Machine
CLC TN92
Type Master's thesis
Year 2009
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In recent years , the wireless network as a new network structure , more and more applications in the campus area , the military field , the medical field and other public places . With the network in various fields of application , also mentioned the importance of research on wireless network management and security agenda . Network traffic analysis and network traffic prediction is one of the important content of network management and security research in the field . This paper analyzes the characteristics of the wireless network traffic , and further study based on a different prediction methods , thus the traffic prediction method which is suitable for wireless network characteristics . In this paper, research from the following aspects : 1) wireless network traffic data analysis . The part of the two major areas of wireless data network characteristics quantitative and qualitative analysis : on the one hand is the flow of data descriptive analysis statistical analysis of wireless network traffic , on the other hand , from the point of view of the probability statistics , sample flow and overall flow distribution characteristics . This part of the analysis behind the establishment of a forecast model provides the basis and foundation . 2) the prediction model of the wireless network traffic . This paper presents a new network traffic prediction the model - GCSVR model . Flow projections as a new model , we will be chaos theory , gray theory and support vector machine three combined for wireless network traffic prediction . Chaos theory is used to analyze the time series , the reconstruction phase space ; gray theory is used to smooth time sequence , weakening rough random characteristics of the data set , the data has strong regularity to the predictive model to provide a more stable and law strong data ; final fitting and forecasting of traffic using support vector machine . By the new model and the comparison of the original SVR model experiments , we found that the prediction accuracy of the new model and predict the step has significantly improved . The experiments show that GCSVR model for sudden strong regularity poor wireless network traffic is not stationary series , able to do a good smooth processing can effectively improve the prediction accuracy and prediction length .

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