Hurst Parameter Estimation and Aggregated Wireless Traffic Properties Analyses Based on Optimal Linear Regression Wavelet Model

In this paper, an optimal linear regression model is proposed to realize accurate and effective estimation of the Hurst parameter in wavelet field, which overcome the shortcomings of traditional R/S statistic, variance-time plots and periodogram-based analysis methods. The aggregated process and statistic characteristics of multiple input traffic sources in WLAN are studied. Simulation results compare the Hurst parameter estimation values of self-similar WLAN traffic by using above statistical approaches and demonstrate that the proposed wavelet method can provide more accurate and efficient estimation of Hurst parameter than traditional statistic analysis approaches in time and frequency domain. Furthermore, simulation results show the aggregate traffic at WLAN also exhibits self-similarity, which actually intensifies rather than diminishes burstiness. These results can be very useful for accurate modeling, traffic control, resources allocation and performance improvement of WLAN