Estimation of multifractal parameters in traffic measurement: An accuracy-based real-time approach

Atzori, Luigi; Aste, N.; Isola, Michela

doi:10.1016/j.comcom.2005.10.024

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…Multifractal theory is a well developed technique and has been used in various fields in analysis and modelling of scaling behaviour of measures/functions in time series processes. Some of the signal and processing analysis based on multifractal include bit error rate process analysis of 11 Mbps wireless MAC-to-MAC channels [22], internet traffic and network traffic measurement estimations [21,23], image feature extraction [24], stock market analysis [25,26], biomedical analysis [27,28] and so on. We stress that in this study, noise model is not being developed, but the study is concerned with investigating multifractal nature of PLC noise and the origins of these multifractals in PLC noise.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Bursty Impulsive Noise in Low-Voltage Indoor Power Line Communication Channels: Local Scaling Behaviour

Asiyo¹,

Afullo²

2017

SAIEE Afr. Res. J.

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Power line communication (PLC) channels are prone to multipath propagation due to impedance mismatch, and impulsive noise whose characteristics are still not well established in the literature. Moreover, measurements show that this impulsive noise appear in bursts, non-Gaussian and cyclostationary and as such cannot be modeled as the convenient additive white Gaussian noise (AWGN). Transceivers optimized for AWGN may not necessarily perform well for the PLC noise. Therefore, investigating the characteristics of PLC noise is very important for accurate modeling of the same. This study presents multifractal analysis of bursty impulsive noise measured from power line networks from three different environments. We employ multifractal detrended fluctuation analysis, which is a well-developed multifractal analysis technique for non-stationary time series data and easy to implement to analyze measured noise data. Results show that power line noise exhibits both long-range dependence (LRD) and multifractal scaling behavior with different strengths depending on the environments where they were captured. The multiscaling behavior is due to long-range correlation inherent in the power line noise. The source of this local multiscaling behavior is determined by analysis a shuffled series of the original data captured from the power network. Multifractal analysis is able to show clearly both the strengths and frequency of occurrence of bursts occurring in PLC noise which can then be applied in accurate modeling of the noise. The significance of these results is that new power line noise models should be developed that captures both LRD and multifractal scaling for more accurate performance evaluation of power line communication systems. The existing noise models though able to replicate temporal dependence of PLC noise, are not able to capture this local scaling behavior which results show is inherent in PLC noise.

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