On the Use of Alpha-Stable Distributions in Noise Modeling for PLC

Laguna-Sanchez, Gerardo; Lopez‐Guerrero, Miguel

doi:10.1109/tpwrd.2015.2390134

Cited by 60 publications

(44 citation statements)

References 14 publications

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“…The α-stable distribution model was commonly employed to model impulsive noise in various physical environments including underwater acoustic noise, man-made audio noise, as well as different types of electromagnetic phenomena [27]. It was used to characterize power line impulsive noise in industrial zone [28] and in other scenarios based on measurement in frequency band of 0-50 kHz in CEN A [29,30]. The α-stable distribution has not yet been examined for the whole frequency band in narrowband PLC.…”

Section: Introductionmentioning

confidence: 99%

Impulsive Noise Characterization in Narrowband Power Line Communication

et al. 2018

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Currently, narrowband Power line communication (PLC) is considered an attractive communication system in smart grid environments for applications such as advanced metering infrastructure (AMI). In this paper, we will present a comprehensive comparison and analysis in time and frequency domain of noise measured in China and Italy. In addition, impulsive noise in these two countries are mainly analyzed and modeled using two probability based models, Middleton Class A (MCA) model and α stable distribution model. The results prove that noise measured in China is rich in impulsive noise, and can be modeled well by α stable distribution model, while noise measured in Italy has less impulsive noise, and can be better modeled by the MCA model.

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Section: Introductionmentioning

confidence: 99%

Impulsive Noise Characterization in Narrowband Power Line Communication

et al. 2018

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“…whereB ,i denotes the ( j, i) element of matrixB. It can be seen that (33) is in the form of (31). Thus, in the steady-state condition of the identification process, (31) is fulfilled.…”

Section: Identification Algorithmmentioning

confidence: 99%

“…In the work of Pearson, different kinds of outliers, several experimental outlier‐contaminated data records, and some specific algorithms to deal with such noises have been presented. In the literature, a large number of models have been utilized for representing outliers, some of which are non‐Gaussian distributions such as α ‐stable noise, t‐distribution, and amplitude‐modulated binary‐state sequence such as Bernoulli‐Gaussian . Furthermore, for the purpose of outlier detection, outlying measurements has been represented deterministically .…”

Section: Introductionmentioning

confidence: 99%

Robust identification of MISO neuro‐fractional‐order Hammerstein systems

Rahmani

Farrokhi

2019

Intl J Robust & Nonlinear

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Summary This paper introduces a multiple‐input–single‐output (MISO) neuro‐fractional‐order Hammerstein (NFH) model with a Lyapunov‐based identification method, which is robust in the presence of outliers. The proposed model is composed of a multiple‐input–multiple‐output radial basis function neural network in series with a MISO linear fractional‐order system. The state‐space matrices of the NFH are identified in the time domain via the Lyapunov stability theory using input‐output data acquired from the system. In this regard, the need for the system state variables is eliminated by introducing the auxiliary input‐output filtered signals into the identification laws. Moreover, since practical measurement data may contain outliers, which degrade performance of the identification methods (eg, least‐square–based methods), a Gaussian Lyapunov function is proposed, which is rather insensitive to outliers compared with commonly used quadratic Lyapunov function. In addition, stability and convergence analysis of the presented method is provided. Comparative example verifies superior performance of the proposed method as compared with the algorithm based on the quadratic Lyapunov function and a recently developed input‐output regression‐based robust identification algorithm.

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“…The result shows that it is not flat, which means the CR sensing system is working in the background of colored noise. That is why the performance of the algorithm mentioned above is declined when applied to practice, as pointed in [19,20]. Therefore, we propose a novel model to describe the non-Gaussian colored noise and present a new detection method to sensing signals.…”

Section: Introductionmentioning

confidence: 99%

Modeling of non-Gaussian colored noise and application in CR multi-sensor networks

Dou

Shi

Lin

et al. 2017

J Wireless Com Network

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Motivated by the practical and accurate demand of intelligent cognitive radio (CR) sensor networks, a new modeling method of practical background noise and a novel sensing scheme are presented, where the noise model is the non-Gaussian colored noise based on α stable process and the sensing method is improved fractional low-order moment (FLOM) detection algorithm with balance parameter. First, we establish the non-Gaussian colored noise model through combining α-distribution with a linear system represented by a matrix. And a fitting curve of practical noise data is given to verify the validity of the proposed model. Then we present a parameter estimation method with low complexity to obtain the balance parameter, which is an important part of the detection algorithm. The balance parameter-based FLOM (BP-FLOM) detector does not require any a priori knowledge about the primary user signal and channels. Monte Carlo simulations clearly demonstrate the performance of the proposed method versus the generalized signal-to-noise ratio, the characteristic exponent α, and the number of detectors in sensing networks.

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On the Use of Alpha-Stable Distributions in Noise Modeling for PLC

Cited by 60 publications

References 14 publications

Impulsive Noise Characterization in Narrowband Power Line Communication

Impulsive Noise Characterization in Narrowband Power Line Communication

Robust identification of MISO neuro‐fractional‐order Hammerstein systems

Modeling of non-Gaussian colored noise and application in CR multi-sensor networks

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