Self-Similarity Parameter Estimation and Scaling Properties Analyses of Aggregated Wireless Traffic

Hurst parameter is important to measure the self-similarity degree and bustiness of network traffic. Based on R/S statistic, variance-time plots and periodogram-based analysis methods, an optimal linear regression wavelet model is proposed to perform the accurate, quick and effective estimation of the Hurst parameter in wavelet field. The aggregating 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 the above statistical approaches. Furthermore, theoretical analyses and simulation results demonstrate that the aggregated 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, resource allocation optimization and performance improvement of WLAN.