Abstract
This paper examines self-similar (or fractal) properties of real teletraffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. The algorithms for modelling fixed-length sequence generators that are used to simulate self-similar behaviour of real teletraffic data in IP traffic for wireless networks are developed and applied. Numerical examples and simulation results are provided.
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