Fast real-time Hurst parameter estimation via adaptive wavelet lifting

Dong Guo, Xiaodong Wang, Junshan Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


A new real-time estimator of the Hurst parameter of a long-range dependent process is developed based on the lifting scheme for wavelet transform. Compared with the existing wavelet-based estimator, the new method performs inplace computation and reduces the computational complexity by about half. We also propose a median-based nonlinear lifting scheme to mitigate the border effects and the noise in the data, by adaptively adjusting the number of vanishing moments of the wavelet. The proposed algorithms are applied to estimate the Hurst parameters in two types of long-range dependent processes, namely, the multiple-access interference in a code-division multiple-access packet data network, and the measurement data of Internet traffic.

Original languageEnglish (US)
Pages (from-to)1266-1273
Number of pages8
JournalIEEE Transactions on Vehicular Technology
Issue number4
StatePublished - Jul 2004

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics


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