TR-COSC 03/99
Fast Self-Similar Teletraffic Generation Based on FGN and Inverse DWT
H.-D. J. Jeong, D. McNickle and K. Pawlikowski
Department of Computer Science
University of Canterbury
Abstract
It is generally accepted that self-similar (or fractal) processes may
provide better models of teletraffic in modern computer networks than
Poisson processes. Thus, an important requirement for conducting
simulation studies of telecommunication networks is the ability to
generate long synthetic stochastic self- similar sequences. A new
generator of pseudo-random self-similar sequences, based on the
fractional Gaussian noise (FGN) and wavelet transform is proposed and
analysed in this paper. Specifically, this generator uses Daubechies
wavelets. The motivation behind this selection of wavelets is that
Daubechies wavelets lead to more accurate results, by matching the
self-similar structure of long range dependent processes. The
statistical accuracy and time required to produce sequences of a given
(long) length are experimentally studied. This generator shows a high
level of accuracy of the output data (in the sense of the Hurst
parameter) and is fast. Its theoretical algorithmic complexity is
O(n).
Keywords : teletraffic generators, complexity, self-similar processes,
fractional Gaussian noise, wavelets, Hurst parameter
