Multifractal Signal Models with Application to Network Traffic

Files in this item

Files Size Format View
Cro1998Non5Multifract.PDF 2.774Mb application/pdf Thumbnail
Cro1998Non5Multifract.PS 232.6Kb application/postscript View/Open

Show full item record

Item Metadata

Title: Multifractal Signal Models with Application to Network Traffic
Author: Crouse, Matthew; Riedi, Rudolf H.; Ribeiro, Vinay Joseph; Baraniuk, Richard G.
Type: Conference paper
xmlui.Rice_ECE.Keywords: multifractal; network traffic modeling
Citation: M. Crouse, R. H. Riedi, V. J. Ribeiro and R. G. Baraniuk, "Multifractal Signal Models with Application to Network Traffic," 1998.
Abstract: In this paper, we develop a new multiscale modeling framework for characterizing positive-valued data with long-range-dependent correlations (1/f noise). Using the Haar wavelet transform and a special multiplicative structure on the wavelet and scaling coefficients to ensure positive results, the model provides a rapid O(N) cascade algorithm for synthesizing N-point data sets. We study both the second-order and multifractal properties of the model, the latter after a tutorial overview of multifractal analysis. We derive a scheme for matching the model to real data observations and, to demonstrate its effectiveness, apply the model to network traffic synthesis. The flexibility and accuracy of the model and fitting procedure result in a close fit to the real data statistics (variance-time plots and moment scaling) and queuing behavior. Although for illustrative purposes we focus on applications in network traffic modeling, the multifractal wavelet model could be useful in a number of other areas involving positive data, including image processing, finance, and geophysics.
Date Published: 1998-08-01

This item appears in the following Collection(s)

  • ECE Publications [1053 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.