Self-similar simulation of IP traffic for wireless networks

Radev, Dimitar; Lokshina, Izabella

doi:10.1504/ijmndi.2007.017324

Cited by 3 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Section 3.2 describes a theoretical method to synthesize self-similar discrete sequences using Haar wavelet-based decomposition. Although Daubechies wavelet-(DW-) based approximations are better in mean and variance, the Haar-based method generates a self-similar sequence of specified Hurst index from practically any given sequence, regardless of whether it is self-similar or not if some conditions are met [19]. For other applications, DW are preferred; see [20] as an example.…”

Section: Discrete Time Self-similaritymentioning

confidence: 99%

“…Expressions (43) and (19) imply that the variance of the th component (̂, , ) (computed using formula (35)), which has also finite length, is…”

Section: Statistics Of the Orthogonal Componentsmentioning

confidence: 99%

“…Finally, the slope ( ) of the plot log Δ versus is obtained (e.g., with a weighted least square regression) and is estimated aŝ=̂/2 + 1. It can be easily proven aŝ2 ( ) = ( −1 ) and substituting it in (16) and (19). The slope is computed by the following weighted formula:…”

Section: Variance Plot-based Estimation Of the Hurst Indexmentioning

confidence: 99%

See 2 more Smart Citations

A Study of Wavelet Analysis and Data Extraction from Second-Order Self-Similar Time Series

Vargas

Roman

Toral-Cruz

2013

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Statistical analysis and synthesis of self-similar discrete time signals are presented. The analysis equation is formally defined through a special family of basis functions of which the simplest case matches the Haar wavelet. The original discrete time series is synthesized without loss by a linear combination of the basis functions after some scaling, displacement, and phase shift. The decomposition is then used to synthesize a new second-order self-similar signal with a different Hurst index than the original. The components are also used to describe the behavior of the estimated mean and variance of self-similar discrete time series. It is shown that the sample mean, although it is unbiased, provides less information about the process mean as its Hurst index is higher. It is also demonstrated that the classical variance estimator is biased and that the widely accepted aggregated variance-based estimator of the Hurst index results biased not due to its nature (which is being unbiased and has minimal variance) but to flaws in its implementation. Using the proposed decomposition, the correct estimation of the Variance Plot is described, as well as its close association with the popular Logscale Diagram.

show abstract

Section: Discrete Time Self-similaritymentioning

confidence: 99%

“…Expressions (43) and (19) imply that the variance of the th component (̂, , ) (computed using formula (35)), which has also finite length, is…”

Section: Statistics Of the Orthogonal Componentsmentioning

confidence: 99%

See 1 more Smart Citation

A Study of Wavelet Analysis and Data Extraction from Second-Order Self-Similar Time Series

Vargas

Roman

Toral-Cruz

2013

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Traffic. Since the seminal work of Leland et al [4], extensive studies have shown that self-similarity manifests in communications traffic in diverse contexts, from local area networks to wide area networks, from wired to wireless applications [5][6][7][8]. In this paper, we consider opportunistic spectrum access in self-similar primary traffic processes with long-range dependency.…”

Section: Opportunistic Spectrum Access In Self-similar Primarymentioning

confidence: 99%

Opportunistic Spectrum Access in Self-Similar Primary Traffic

Xiao

Liu

Zhao

2009

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

We take a stochastic optimization approach to opportunity tracking and access in self-similar primary traffic. Based on a multiple timescale hierarchical Markovian model, we formulate opportunity tracking and access in self-similar primary traffic as a Partially Observable Markov Decision Process. We show that for independent and stochastically identical channels under certain conditions, the myopic sensing policy has a simple round-robin structure that obviates the need to know the channel parameters; thus it is robust to channel model mismatch and variations. Furthermore, the myopic policy achieves comparable performance as the optimal policy that requires exponential complexity and assumes full knowledge of the channel model.

show abstract