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    Fast Self-Similar Teletraffic Generation Based on FGN and Inverse DWT

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    Author
    Jeong, H-D.J.
    McNickle, D.
    Pawlikowski, K.
    Date
    1999
    Permanent Link
    http://hdl.handle.net/10092/3081

    It is generally accepted that self-similar (or fractal) processes may provide better models of teletra c 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 selfsimilar 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. Speci cally, 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).

    Subjects
    teletraffic generators
     
    complexity
     
    self-similar processes
     
    fractional Gaussian noise
     
    wavelets
     
    Hurst parameter
     
    Fields of Research::280000 Information, Computing and Communication Sciences
     
    Fields of Research::290000 Engineering and Technology::291700 Communications Technologies::291704 Computer communications networks
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    • Engineering: Reports [684]
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