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Modeling and Analysis of Wireless Lan Traffic  

Yamkhin, Dashdorj (한양대학교 전자컴퓨터통신공학과 분산멀티미디어 연구실)
Lee, Seong-Jin (한양대학교 전자컴퓨터통신공학과 분산멀티미디어 연구실)
Won, You-Jip (한양대학교 전자컴퓨터통신공학과 분산멀티미디어 연구실)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.33, no.8B, 2008 , pp. 667-680 More about this Journal
Abstract
In this work, we present the results of our empirical study on 802.11 wireless LAN network traffic. We collect the packet trace from existing campus wireless LAN infra-structure. We analyzed four different data sets: aggregate traffic, upstream traffic, downstream traffic, tcp only packet trace from aggregate traffic. We analyze the time series aspect of underlying traffic (byte count process and packet count process), marginal distribution of time series, and packet size distribution. We found that in all four data sets there exist long-range dependent property in byte count and packet count process. Inter-arrival distribution is well fitted with Pareto distribution. Upstream traffic, i.e. from the user to Internet, exhibits significant difference in its packet size distribution from the rests. Average packet size of upstream traffic is 151.7 byte while average packet size of the rest of the data sets are all greater than 260 bytes. Packets with full data payloads constitutes 3% and 10% in upstream traffic and the downstream traffic, respectively. Despite the significant difference in packet size distribution, all four data sets have similar Hurst values. The Hurst alone does not properly explain the stochastic characteristics of the underlying traffic. We model the underlying traffic using fractional-ARIMA (FARIMA) and fractional Gaussian Noise (FGN). While the fractional Gaussian Noise based method is computationally more efficient, FARIMA exhibits superior performance in accurately modeling the underlying traffic.
Keywords
Modeling; Analysis; Self-similarity; Long-range Dependenc; Fractional-ARIMA; Fractional Gaussian Noise;
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