A Study on Impulse Noise and Its Models

Shongwe, Thokozani; Vinck, A. J. Han; Ferreira, H.C.

doi:10.23919/saiee.2015.8531938

Cited by 72 publications

(53 citation statements)

References 47 publications

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“…The noise scenario in PLC environments involves multiple parameters that define the origin of the noise, its magnitude, and its time‐domain representation . From this point of view, the PLC noise can be grouped into three types that are the background noise, the narrowband interference, and the impulsive noise . Due to its harshness, the PLC noise has been widely studied.…”

Section: The Plc Channel and Its Modelsmentioning

confidence: 99%

Power line communications (PLC) technology: More than 20 years of intense research

Ndjiongue

Ferreira

2019

Trans Emerging Tel Tech

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After more than two decades since intensive research began on power line communications (PLC) technology, this interesting field of telecommunication engineering is still progressing towards maturity. Despite some factors that delay its worldwide acceptance, its implementation is starting to take off. Research in PLC is motivated (i) by many factors such as the crowded radio frequency spectrum that requires to be sustained by alternative technologies and (ii) by the emanation of other new technologies that may help PLC to extend its practical implementation such as visible light communications or other wireless communication technologies like near‐field communications. PLC technology is used in applications requiring both low and high data rate but traditionally exploited in applications in which the node does not move. Nowadays, PLC has become widely accepted; hence, a review of the technology may be useful to keep the readers aware of new updates. In this paper, we provide an overview of the evolution of PLC technology over the last few decades.

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Section: The Plc Channel and Its Modelsmentioning

confidence: 99%

Power line communications (PLC) technology: More than 20 years of intense research

Ndjiongue

Ferreira

2019

Trans Emerging Tel Tech

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“…Hence, it is convenient not to distinguish the observation error from background noise, but to simply assume that an uncontaminated observation sequence of X is available, as usually done in the field of noise characterization e.g. [4], [9], [17].…”

Section: B Approximations In Underspread Channelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Compared to other common impulsive noise models such like the Middleton's Class-A (MCA) model [7] and the symmetric α-stable (SαS) model [8], the BG model stands out with its compatibility to different noise bandwidths, while simultaneously exhibiting a heavy-tailed probability density function (PDF) with a simple closed-form expression. Moreover, it can be easily extended to the Markov-Gaussian model to characterize noise bursts [9].However, despite these superiorities, the deployment of BG model in communications and signal processing has been limited by the insufficient study on its parameter estimation, or more specifically, on the impulse detection. Unlike the MCA/SαS models that describe the overall statistics of the mixed noise, the BG model separates impulses from the…”

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confidence: 99%

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A Fast Blind Impulse Detector for Bernoulli-Gaussian Noise in Underspread Channel

Han

Schotten

2018

2018 IEEE International Conference on Communications (ICC)

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Impulsive noises widely existing in various channels can significantly degrade the performance and reliability of communication systems. The Bernoulli-Gaussian (BG) model is practical to characterize noises in this category. To estimate the BG model parameters from noise measurements, a precise impulse detection is essential. In this paper, we propose a novel blind impulse detector, which is proven to be fast and accurate for BG noise in underspread communication channels. I. INTRODUCTIONImpulsive noises are widely observed in various communication systems, including ultra wide-band (UWB) systems [1], wireless local area networks (WLAN) [2], digital subscriber line (DSL) networks [3] and power line communication (PLC) systems [4]. Due to their non-stationary nature and high peak power, they can significantly degrade the performance and reliability of communication systems. Such impacts can become critical in urban and industrial environments where 1) frequent mechanical switching operations and vibrations are present to produce dense impulsive noises, and 2) ultra-high reliability and ultra-low latency are expected in short-range wired/wireless communication applications. Various use cases of this kind have been addressed in scopes of both the Fifth Generation (5G) mobile networks [5] and new advanced industrial communication solutions [6]. In these contexts, techniques of modeling impulsive noises are required as a major tool to define channel models and thereby develop robust communication systems.The Bernoulli-Gaussian (BG) model has been widely applied on impulsive noises. Compared to other common impulsive noise models such like the Middleton's Class-A (MCA) model [7] and the symmetric α-stable (SαS) model [8], the BG model stands out with its compatibility to different noise bandwidths, while simultaneously exhibiting a heavy-tailed probability density function (PDF) with a simple closed-form expression. Moreover, it can be easily extended to the Markov-Gaussian model to characterize noise bursts [9].However, despite these superiorities, the deployment of BG model in communications and signal processing has been limited by the insufficient study on its parameter estimation, or more specifically, on the impulse detection. Unlike the MCA/SαS models that describe the overall statistics of the mixed noise, the BG model separates impulses from the

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“…This fuelled studies to determining the major sources of noise in the power line networks and their associated noise characteristics, studies which are well detailed in [3,6,7]. Besides such studies, recent surveys in PLC noise models have also grouped the noise models into models with memory and memoryless models [8,9]. Because of the bursty nature of the impulsive noise in the power line network, it is also referred to as bursty impulsive noise [10,11].…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis of Impulsive Noise in Power Line Communication (PLC) Networks

Awino¹,

Afullo²,

Mosalaosi³

et al. 2018

SAIEE Afr. Res. J.

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This paper proposes and discusses Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models for broadband power line communication (PLC) networks with impulsive noise enviroment in the frequency range of 1-30 MHz. In time series modelling and analysis, time series models are fitted to the acquired time series describing the system for purposes which include simulation, forecasting, trend assessment, and a better understanding of the dynamics of the impulsive noise in PLC systems. Also, because the acquired impulsive noise measurement data are observations made over time, time series models constitute important statistical tools for use in solving the problem of impulsive noise modelling and forecasting in PLC. In fact, the time series and other statistical methods presented in numerous available literature draw upon research developments from two areas of environmetrics called stochastic hydrology and statistical water quality modelling as well as research contributions from the field of statistics. In time series modelling and analysis, we determine the most appropriate stochastic or time series model to fit our acquired data set at the confirmatory data analysis stage. No matter what type of stochastic model is to be fitted to the data set, we follow the identification, estimation, and diagnostic check stages of model construction. In addition, we explore the resulting autocorrelation functions in estimating the parameters of the selected time series models. Finally, SARIMA model is found suitable for computer-based PLC systems simulations and forecasting based on the diagnostic checks.

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A Study on Impulse Noise and Its Models

Cited by 72 publications

References 47 publications

Power line communications (PLC) technology: More than 20 years of intense research

Power line communications (PLC) technology: More than 20 years of intense research

A Fast Blind Impulse Detector for Bernoulli-Gaussian Noise in Underspread Channel

Time Series Analysis of Impulsive Noise in Power Line Communication (PLC) Networks

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