Presence Of Impulsive Noise Research Articles

Generalized Variable Step Size Continuous Mixed ${p}$ -Norm Adaptive Filtering Algorithm

To further improve the performance of the variable step size continuous mixed p-norm (VSS-CMPN) adaptive filtering algorithm in the presence of impulsive noise, a generalized VSS-CMPN algorithm is proposed in this brief. Instead of assuming the probability density-like function λ(p) to be uniform, a linear function is proposed for λ(p) to control the mixture of various error norms. The influence of the selection of the regulating factor (slope of the linear function) is discussed. Besides, the computational complexity as well as the mean-square convergence analysis is presented. Simulations conducted in the system identification scenario demonstrate the superiority of the proposed algorithm over known algorithms.

On the performance of coded copper wire systems for G-fast communications

<p>This paper aims to investigate the performance of different channel coding techniques over copper wire channels. First, Chen model is used to simulate the copper wire channel model, then the performance of LDPC coded system with DVB-S2 standard is compared to the performance of turbo coded, and convolutional coded systems over wired channels. In addition, the performance of the systems under consideration is further investigated in the presence of impulsive noise (IN) with IN cancellation technique. Matlab simulations are utilized to simulate these systems for different quadrature amplitude modulations (QAM). The results have shown that LDPC coded system with large block length have outperforms the performance of the considered coding techniques.</p>

Group sparse recovery in impulsive noise via alternating direction method of multipliers

In this paper, we consider the recovery of group sparse signals corrupted by impulsive noise. In some recent literature, researchers have utilized stable data fitting models, like l 1 -norm, Huber penalty function and Lorentzian-norm, to substitute the l 2 -norm data fidelity model to obtain more robust performance. In this paper, a stable model is developed, which exploits the generalized l p -norm as the measure for the error for sparse reconstruction. In order to address this model, we propose an efficient alternative direction method of multipliers, which includes the proximity operator of l p -norm functions to the framework of Lagrangian methods. Besides, to guarantee the convergence of the algorithm in the case of 0 ≤ p < 1 (nonconvex case), we took advantage of a smoothing strategy. For both 0 ≤ p < 1 (nonconvex case) and 1 ≤ p ≤ 2 (convex case), we have derived the conditions of the convergence for the proposed algorithm. Moreover, under the block restricted isometry property with constant δ τ k 0 < τ / ( 4 − τ ) for 0 < τ < 4 / 3 and δ τ k 0 < ( τ − 1 ) / τ for τ ≥ 4 / 3 , a sharp sufficient condition for group sparse recovery in the presence of impulsive noise and its associated error upper bound estimation are established. Numerical results based on the synthetic block sparse signals and the real-world FECG signals demonstrate the effectiveness and robustness of new algorithm in highly impulsive noise.

Robust Compressive Two-Dimensional Near-Field Millimeter-Wave Image Reconstruction in Impulsive Noise

In this letter, a novel robust two-dimensional nearfield millimeter-wave (MMW) image reconstruction algorithm is developed based on compressed sensing (CS) in the presence of impulsive measurement noise. To enable the sparse MMW image recovery, the CS algorithm usually employs the popular l 2 -norm as the data fidelity term and a combination of multiple sparsityinduced functions as the penalty term. The l 2 -norm is non-robust against impulsive noise since the Gaussian noise distribution assumption is not valid, and the presence of impulsive noise will severely degrade the robustness of the compressive MMW image recovery. To gain more robustness performance, a complex correntropy based data-fitting term, namely complex correntropic loss (CC-loss), is used to replace the l2-norm for the near-field MMW measurement contaminated by impulsive noise. In order to solve the corresponding minimization problem, an additive half quadratic method is used to transform the CC-loss term to its convex form, and then a parallel primal-dual process is used to guarantee the convergence of the proposed algorithm. In total, 150-GHz MMW experimental results show that the proposed algorithm can achieve accurate image reconstruction from compressive measurements under different impulsive noise levels.

Performance Analysis of Analog Intermittently Nonlinear Filter in the Presence of Impulsive Noise

In this paper, an adaptive nonlinear differential limiter (ANDL) is proposed to efficiently alleviate the impact of impulsive noise (IN) in a communication system. Unlike existing nonlinear methods, the ANDL is implemented in the analog domain where the broader acquisition bandwidth makes outliers more detectable and consequently it is easier to remove them. While the proposed ANDL behaves like a linear filter when there is no outlier, it exhibits intermittent nonlinearity in response to IN. Therefore, the structure of the matched filter in the receiver is modified to compensate the filtering effect of the ANDL in the linear regime. In this paper, we quantify the performance of the ANDL by deriving a closed-form analytical bound for the average signal-to-noise ratio at the output of the filter. The calculation is based on the idea that the ANDL can be perceived as a time-variant linear filter whose bandwidth is modified based on the intensity of the IN. In addition, by linearizing the filter time parameter variations, we treat the ANDL as a set of linear filters where the exact operating filter at a given time depends upon the magnitude of the outliers. The theoretical average bit error rate is validated through simulations and the performance gains relative to classical methods such as blanking and clipping are quantified.

Wavelet OFDM with Overlap FDE for non-Gaussian channels in precoded NOMA based systems

Non-orthogonal multiple access (NOMA) is considered as a prominent multiple access technique for fifth generation (5G) systems mainly because of its high spectral efficiency (SE). NOMA based system employs superposition coding (SC) and successive interference cancellation (SIC) at the transmitter and receiver side respectively. Classical multi-carrier NOMA adopts orthogonal frequency division multiplexing (OFDM) as a multi-carrier modulation (MCM) technique; however, high peak to average power ratio (PAPR) and cyclic prefix (CP) inclusion makes OFDM an inadequate choice for NOMA. To improve SE of the OFDM-NOMA, wavelet transform (WT) has been proposed for NOMA. This article proposes new physical layer techniques based on OFDM-NOMA and Wavelet OFDM (WOFDM) -NOMA for tactile internet and investigates their performance in the presence of Gaussian and non-Gaussian channels. Overlap frequency domain equalization (OFDE) is proposed as a robust equalization technique to improve the performance of SIC in the presence of Gaussian and non-Gaussian channels for OFDM-NOMA and WOFDM-NOMA. Moreover, to enhance the equalization efficiency of the OFDE, linear precoding (LP) in the form of Walsh Hadamard transform (WHT) is also recommended for OFDM and WOFDM based NOMA transceivers which improves the reliability of the network. Performance of the proposed precoded OFDM-NOMA and precoded WOFDM-NOMA with OFDE is analyzed in the presence of additive white Gaussian noise (AWGN) and impulse noise for Rayleigh fading channel. Link level performance of the presented structures for downlink NOMA is evaluated in terms of bit error rate (BER), PAPR, SE and computational complexity (CC). Furthermore, performance of the proposed architectures is also compared in the presence of impulse noise having different intensities. Simulation results via computer show that the proposed precoded WOFDM-NOMA with OFDE transceiver incorporates low latency through waveform shaping and enhanced reliability through precoding and efficient equalization. Thus, proposed advanced physical layer not only performs better than its counterparts but presence of OFDE enhances SIC performance even in the presence of impulse noise at the expense of slight elevation in CC.

Bounded non-linear covariance based ESPRIT method for noncircular signals in presence of impulsive noise

Among the most important approaches in the study of DOA estimation, ESPRIT-like methods have received considerable attentions and have been widely applied in practical applications. Although many of the latest approaches have considered various performance requirements, the robustness to impulsive noise warrants further investigation. Inspired by the idea of bounded non-linear covariance (BNC), a novel subspace based method for DOA estimation is proposed in this paper. Named NC-BNC-ESPRIT, this method uses the BNC matrix to create a signal subspace of extended array outputs and it can handle the DOA estimation for noncircular (NC) signals in presence of impulsive noise. Simulation experiments and theoretical analysis are provided to verify this methods' superiority over existing approaches and proof of its robustness is provided in the appendix.

An efficient algorithm for nonlinear active noise control of impulsive noise

Nonlinear active noise control (NANC) systems employing Volterra filter suffer from the stability issues in the presence of impulsive noise. To solve this problem, we combine the second-order Volterra (SOV) filter and maximum correntropy criterion (MCC) in this paper. The Volterra filter-x maximum correntropy criterion (VF × MCC) algorithm and Volterra filter-x recursive maximum correntropy (VF × RMC) algorithm are applied to reduce the impulsive noise of NANC. We find that VF × MCC algorithm has a low computational complexity and VF × RMC algorithm converges fast. In order to extract their advantages, we further propose a hybrid algorithm based on the VF × MCC and VF × RMC algorithms. In addition, the normalize step-size version of VF × MCC (VF × nMCC) algorithm is developed to improve the robustness and performance. Meanwhile, we adaptively adjust the kernel size of MCC online based on the sample variance of reference signal to improve the performance of the proposed algorithms. Simulation results in the context of nonlinear active impulsive noise control demonstrate that the proposed algorithms achieve much better performance than the existing algorithms in various noise environments.

Rolling Element Bearing Fault Diagnosis under Impulsive Noise Environment Based on Cyclic Correntropy Spectrum.

Rolling element bearings are widely used in various industrial machines. Fault diagnosis of rolling element bearings is a necessary tool to prevent any unexpected accidents and improve industrial efficiency. Although proved to be a powerful method in detecting the resonance band excited by faults, the spectral kurtosis (SK) exposes an obvious weakness in the case of impulsive background noise. To well process the bearing fault signal in the presence of impulsive noise, this paper proposes a fault diagnosis method based on the cyclic correntropy (CCE) function and its spectrum. Furthermore, an important parameter of CCE function, namely kernel size, is analyzed to emphasize its critical influence on the fault diagnosis performance. Finally, comparisons with the SK-based Fast Kurtogram are conducted to highlight the superiority of the proposed method. The experimental results show that the proposed method not only largely suppresses the impulsive noise, but also has a robust self-adaptation ability. The application of the proposed method is validated on a simulated signal and real data, including rolling element bearing data of a train axle.

Robust Diffusion Affine Projection M-Estimate Algorithm for Distributed Estimation over Network

In this paper, a robust diffusion affine projection M-estimate (DAPM) algorithm is proposed for distributed estimation in the adaptive diffusion network. To eliminate the adverse effects of impulsive noise in case of the impulsive interference environment on the filter weight updates, this algorithm uses a robust cost function based on M-estimate function and is derived by the steepest-descent method. Simulation results verify that the proposed DAPM algorithm is effective for system identification scenarios in the presence of impulsive noise.

Intermittently Nonlinear Impulsive Noise Mitigation and Doppler Shift Compensation in UWA-OFDM Systems

Impulsive noise (IN) and Doppler shift can significantly degrade the performance of orthogonal frequency-division multiplexing (OFDM)-based underwater acoustic (UWA) communication systems. In this paper, we propose a receiver structure that deals efficiently with both these channel impairments in a coded OFDM-based UWA system. First, an analog nonlinear preprocessor (ANP) is proposed to efficiently detect and mitigate IN in an analog domain. The proposed ANP exhibits intermittent nonlinearity when there is impulsivity. Next, the impact of IN on a two-step Doppler shift compensation approach is quantified. Specifically, the ability of the ANP to improve the robustness of Doppler shift compensation in the presence of IN is highlighted. The performance improvement of the proposed receiver is due to the fact that unlike the other nonlinear methods, the ANP is implemented in the analog domain where the outliers are still broadband and distinguishable. The simulation results also demonstrate the superior bit-error-rate (BER) performance of our approach relative to classic approaches that use blanking and/or clipping for IN mitigation.

Robust Distributed Diffusion Recursive Least Squares Algorithms With Side Information for Adaptive Networks

This work develops a robust diffusion recursive least squares algorithm to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. This algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate estimate update at each node. With the help of side information, the constraint is recursively updated in a diffusion strategy. Moreover, a control strategy for resetting the constraint is also proposed to retain good tracking capability when the estimated parameters suddenly change. Simulations show the superiority of the proposed algorithm over previously reported techniques in various impulsive noise scenarios.

Linear phase sparse system identification in the presence of impulsive noise

ABSTRACTIn this paper, we incorporate an ℓp-norm sparsity constraint into linear phase constrained affine projection-sign algorithm (LPCAP-SA). The proposed algorithm combines the effect of linear phase constrained affine projection sign algorithm and ℓp-norm sparsity constraint .The proposed algorithm has better performance for linear phase sparse system identification in the presence of vigorous impulsive interference. The proposed ℓp-norm LPCAP-SA is based on the ℓ1-norm minimisation criterion on posterior error and ℓp-Norm on weight coefficients and a linear phase constraint. Simulations in Matlab software show that the proposed ℓp-norm LPCAP-SA has robustness against impulsive interferences and sparse environment for linear phase system.

Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm.

The maximum complex correntropy criterion (MCCC) has been extended to complex domain for dealing with complex-valued data in the presence of impulsive noise. Compared with the correntropy based loss, a kernel risk-sensitive loss (KRSL) defined in kernel space has demonstrated a superior performance surface in the complex domain. However, there is no report regarding the recursive KRSL algorithm in the complex domain. Therefore, in this paper we propose a recursive complex KRSL algorithm called the recursive minimum complex kernel risk-sensitive loss (RMCKRSL). In addition, we analyze its stability and obtain the theoretical value of the excess mean square error (EMSE), which are both supported by simulations. Simulation results verify that the proposed RMCKRSL out-performs the MCCC, generalized MCCC (GMCCC), and traditional recursive least squares (RLS).

On channel estimation for power line communication systems in the presence of impulsive noise

In this paper, two maximum-likelihood (ML) channel estimators that are robust and perform consistent channel estimation in the presence of impulsive noise (IN) for power line communication (PLC) systems are proposed and analyzed. The two estimators differ on the way they exploit the estimated IN to determine the coefficients of the channel impulse response. In the first approach, the channel estimator treats the estimated IN as a deterministic quantity; in the second as a random quantity. The performances of both estimators are analyzed and numerically evaluated. As it will be shown, between the two proposed estimators, the one that is based on the random approach outperforms the deterministic one in all typical PLC scenarios. However, the deterministic approach-based estimator can perform consistent channel estimation regardless of the IN behavior with less computational effort and becomes an efficient channel estimation strategy in situations where high computational complexity cannot be afforded.

Random Fourier Filters Under Maximum Correntropy Criterion

Random Fourier adaptive filters (RFAFs) project the original data into a high-dimensional random Fourier feature space (RFFS) such that the network structure of filters is fixed while achieving similar performance with kernel adaptive filters. The commonly used error criterion in RFAFs is the well-known minimum mean-square error (MMSE) criterion, which is optimal only under the Gaussian noise assumption. However, the MMSE criterion suffers from instability and performance deterioration in the presence of non-Gaussian noises. To improve the robustness of RFAFs against large outliers, the maximum correntropy criterion (MCC) is applied to RFFS, generating a novel robust random Fourier filter under maximum correntropy (RFFMC). To further improve the filtering accuracy, a random-batch RFFMC (RB-RFFMC) is also presented. In addition, a theoretical analysis on the convergence characteristics and steady-state excess mean-square error of RFFMC and RB-RFFMC is provided to validate their superior performance. Simulation results illustrate that RFFMC and its extension provide desirable filtering performance from the aspects of filtering accuracy and robustness, especially in the presence of impulsive noises.

A Robust Joint Estimation Method of Time Delay and Doppler Frequency Shift

In this paper, a novel concept, the Sigmoid transform based cyclic correlation, is proposed and a relative estimator named the Sigmoid transform based cyclic cross-ambiguity function is also derived to handle the joint estimation problem in the presence of impulsive noise and co-channel interference. Based on these definitions, a novel joint estimation method of the time delay and the Doppler frequency shift is proposed. Simulations have verified its superior performances over existing methods based on cyclic ambiguity function or generalized fractional lower-order cyclic cross-ambiguity function, especially under impulsive noise. Meanwhile, it is more applicable for practical applications since it is parameter-free.

An Improved Variable Regularization Parameter for Sign Subband Adaptive Filter

This paper proposes an improved variable regularization parameter (VRP) for sign subband adaptive filter, which is derived on the basis of the mean-square deviation (MSD) analysis. Since it is difficult to get access to the exact values of some quantities in the MSD, the VRP is calculated by minimizing the upper bound of a certain term which is employed to update the MSD. Moreover, a re-initialization mechanism that can enhance the tracking capability is developed. We also discuss the computational complexity of our finding. The proposed scheme enjoys more merits than several prior algorithms, which is illustrated by sufficient simulations designed for both the system identification and acoustic echo cancelation applications in the presence of impulsive noise.

Blind deconvolution based on cyclostationarity maximization and its application to fault identification

Blind deconvolution algorithms prove to be effective tools for fault identification, being able to extract excitation sources from noisy observations only. In this scenario, the present paper introduces a novel blind deconvolution method based on the generalized Rayleigh quotient and solved by means of an iterative eigenvalue decomposition algorithm. This approach not only is characterized by a weighting matrix that drives the deconvolution process, but can also be easily adapted to arbitrary criteria. Based on this framework, a novel criterion rooted on the maximization of the cyclostationarity of the excitation – as typically encountered with machine faults – is proposed and compared with other blind deconvolution methods existing in the literature. The comparisons involve both synthesized and real vibration signals, taking into account a gear tooth spall and an outer race bearing fault. The results reveal superior capability to recover impulsive cyclostationary sources with respect to other blind deconvolution methods, even in the presence of impulsive noise or under non-constant speed.

Performance Analysis of Distributed Wireless Sensor Networks for Gaussian Source Estimation in the Presence of Impulsive Noise

Abstract—We address the distributed estimation of a scalar Gaussian source in wireless sensor networks (WSNs). The sensor nodes transmit their noisy observations, using the amplifyand- forward relaying strategy through coherent multiple access channel to the fusion center (FC) that reconstructs the source parameter. In this letter, we assume that the received signal at the FC is corrupted by impulsive noise and channel fading, as encountered for instance within power substations. Over Rayleigh fading channel and in presence of Middleton class-A impulsive noise, we derive the minimum mean square error (MMSE) optimal Bayesian estimator along with its mean square error (MSE) performance bounds. From the obtained results, we conclude that the proposed optimal MMSE estimator outperforms the linear MMSE estimator developed for Gaussian noise scenario. Index Terms—Distributed WSN, MMSE optimal Bayesian estimation, Middleton class-A impulsive noise, Rayleigh fading.