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dc.contributor.authorRiedi, Rudolf H.
Ribeiro, Vinay Joseph
Crouse, Matthew
Baraniuk, Richard G.
dc.creatorRiedi, Rudolf H.
Ribeiro, Vinay Joseph
Crouse, Matthew
Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T01:01:30Z
dc.date.available 2007-10-31T01:01:30Z
dc.date.issued 2000-07-01
dc.date.submitted 2000-07-01
dc.identifier.citation R. H. Riedi, V. J. Ribeiro, M. Crouse and R. G. Baraniuk, "Network Traffic Modeling using a Multifractal Wavelet Model," 2000.
dc.identifier.urihttps://hdl.handle.net/1911/20275
dc.description Conference Paper
dc.description.abstract In this paper, we develop a simple and powerful multiscale model for syntheizing nonFaussian, long-range dependent (LRD) network traffic. Although wavelets effectively decorrelate LRD data, wavelet-based models have generally been restricted by a Gaussianity assumption that can be un-realistic for traffic. Using a multiplicative superstructure on top of the Haar wavelet transform, we exploit the decorrelating properties of wavelets while simultaneously capturing the positivity and "spikiness" of nonGaussian traffic. This leads to a swift O(N) algorithm for fitting and synthesizing N-point data sets. The resulting model belongs to the class of multifractal cascades, a set of processes with rich statistical properties. We elucidate our model's ability to capture the covariance structure of real data and then fit it to real traffic traces. Queueing experiments demonstrate the accuracy of the model for matching real data.
dc.description.sponsorship Texas Instruments
dc.description.sponsorship Defense Advanced Research Projects Agency
dc.description.sponsorship Office of Naval Research
dc.description.sponsorship National Science Foundation
dc.language.iso eng
dc.subjectlong range dependent network traffic
dc.subject.otherDSP for Communications
dc.title Network Traffic Modeling using a Multifractal Wavelet Model
dc.type Conference paper
dc.date.note 2006-07-19
dc.citation.bibtexName inproceedings
dc.date.modified 2006-07-24
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordlong range dependent network traffic
dc.citation.location Barcelona, Spain
dc.citation.conferenceName European Congress of Mathematics
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-0348-8266-8_54
dc.citation.firstpage 609
dc.citation.lastpage 618


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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1426]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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