A Multiscale Analysis and Modeling of Wireless Traffic

Performance predictions from wireless networking laboratory experiments seem to dismatch what is really deployed. To generate realistic traffic is one of the major factors hampering researcherspsila ability to make reliable forecasts. This paper reports on the results of a multiscale analysis of traffic traces captured at an infrastructure 802.11g wireless network. The analyzed traffic is characterized by a non self-similar process for small time scales and strong self-similarity for larger time scales. For smaller time scales, the packet arrival times are independent explaining the weak correlation of the data. For larger timescales the arrival of TCP connections can be considered independent. Additionally, we propose a model which allows generating traffic similar to the measured traffic. The model contains only multiscale index allowing influencing all observed changes in LD and variance-time plots. Comparing the output of the model with the real traffic traces we find that the model matches reality accurately.