Wavelet analysis-based long range dependence in WiMax traffic

被引：0

作者：

Hu, Yongdong ^{[1
,2
,3
]}

Wu, Guoxin ^{[1
,2
]}

Xu, Yiqing ^{[1
,2
,3
]}

机构：

[1] School of Computer Science and Engineering, Southeast University, Nanjing 211189, China

[2] Key Laboratory of Computer Network and Information Integration of Ministry of Education, Southeast University, Nanjing 211189, China

[3] College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China

来源：

Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2013年 / 43卷 / 01期

关键词：

Hurst parameter - Long range dependence - Non-real time traffics - Real time traffics - Scale-invariance property - Theoretical values - Wavelet analysis method - Wavelet coefficients;

D O I：

10.3969/j.issn.1001-0505.2013.01.001

中图分类号：

学科分类号：

摘要：

In order to detect long-range dependence (LRD) in the WiMax network traffic accurately and efficiently, using the scale invariance property of self-similar stochastic process and the wavelet transform, the statistics of LRD Hurst parameters and wavelet coefficients are established. Then, the wavelet analysis algorithm of LRD in network traffic is studied. Repeated selection of wavelet vanishing moments and the optimization of the range of the scale parameter can reduce the impact on the test results. By the ON/OFF model, five different types of traffic in WiMax networks are simulated. And then the wavelet analysis method is used to analyze the trace file. The test results show that LRD of the real-time traffic is small, but LRD of non-real-time traffic is relatively large. As the network load increases, LRD of network traffic may change slightly. The detection value of LRD of aggregate flow is basically consistent with the theoretical value.

引用

页码：1 / 5

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