Impulsive Noise Probability Research Articles

DOA estimation under Bernoulli-Gaussian impulsive noise

Direction of arrival algorithms have ability to distinguish between directions of individual sources under additive white Gaussian noise. However, presence of the impulsive noise can corrupt the angles estimation. In this paper, the performance of MUSIC and ESPRIT, as estimation algorithms, is investigated in impulsive noise with white Gaussian noise environment. The impulsive noise is modelled using Bernoulli-Gaussian model and the effect of varying its statistical parameters on the estimation ability is tested. Three sources with different angles are simulated and a uniform linear array is utilized to capture the impinging signals. The performance analysis includes impulsive noise probability of occurrence and amplitude intensity which both control the impulsive power. The mean square error of the estimated angles is adopted as a metric to measure the performance. The simulation results provide a map of all the expected possible impulsive noise cases which influence the MUSIC and ESPRIT direction of arrival algorithms. The expected cases show that the performance degradation happening in ESPRIT is more than that of MUSIC. At 0 dB SNR, the impulsive noise increases the mean square error by almost 15 dB and 20 dB for MUSIC and ESPRIT, respectively.

Algorithm for Estimating Type and Probability of Impulse Noise in Color Images

Abstract—Color digital images are often contaminated by impulse noise during transmitting and recording of information. Efficient methods for elimination of impulse noise are needed to improve image quality. An algorithm for estimating of the type and probability of impulse noise in color images using morphological operations is proposed. Results of computer experiments are presented to demonstrate the performance of the proposed algorithm for estimating of the type and probability of impulse noise for several interference models.

Energy-Per-Bit Performance Analysis of Relay-Assisted Power Line Communication Systems

This paper provides a comprehensive analysis of the energy efficiency performance for different relaying schemes over the non-Gaussian power line communication (PLC) channel. Specifically, amplify-and-forward (AF), decode-and-forward (DF), selective DF (SDF) and incremental DF (IDF) relaying systems are investigated. For a more realistic scenario, the power consumption profile of the PLC modems is assumed to consist of both dynamic and static power. For each system, we derive accurate analytical expressions for the outage probability and the minimum energy-per-bit performance. For the sake of comparison and completeness as well as to quantify the achievable gains, we also analyze the performance of a single-hop PLC system. Monte Carlo simulations are provided throughout this paper to validate the theoretical analysis. Results reveal that AF relaying over the non-Gaussian PLC channel does not always enhance the performance and that the IDF PLC system offers the best performance compared to all other schemes considered. It is also shown that increasing the channel variance, which is related to the PLC network branching, and impulsive noise probability can considerably deteriorate the system performance. Furthermore, when the end-to-end distance is relatively small, it is found that the single-hop PLC approach can perform better than AF relaying.

Two-Stage Non-Orthogonal Multiple Access Over Power Line Communication Channels

Non-orthogonal multiple access (NOMA) has recently been proposed for dual-hop cooperative relaying power line communication (PLC) systems. Unlike conventional NOMA-PLC schemes which deploy NOMA only at the relay, this paper proposes to enhance the performance of such systems by implementing the principle of NOMA at both the source and relaying modems. The system performance is evaluated in terms of the average sum capacity for which analytical expressions are derived for both the improved and conventional NOMA-PLC systems. Throughout our analysis, the PLC channel is assumed narrow-band modeled with log-normal amplitude distribution and the total PLC noise consists of both background and impulsive noise. Monte Carlo simulations are provided to corroborate the accuracy of our theoretical analysis. The derived expressions are utilized to examine the impact of various system parameters on the average capacity performance; this includes: impulsive noise probability, network branching, power allocation coefficients, and transmit power. The optimization problem of the power allocation coefficients is also addressed for both NOMA-PLC systems under consideration. Results reveal that significant gains in the average capacity can be attained with the improved NOMA-PLC approach compared to the conventional system. In addition, the improved system is able to meet a given performance requirement with smaller transmit power offering more relaxed electromagnetic compatibility issues associated with PLCs. Finally, it is demonstrated that optimizing the power allocation coefficients at both the source and relay modems is crucial to maximize performance.

On the Performance of Blanking Nonlinearity in Real-Valued OFDM-Based PLC

To mitigate the impulsive noise in orthogonal frequency-division multiplexing (OFDM)-based power-line communications (PLC), a blanker can be employed at the receiver. In the literature, the performance of the blanker has been discussed and analyzed thoroughly. However, the analysis was limited only to passband OFDM PLC systems with complex-valued baseband representation. This paper aims to derive a mathematical expression of the output signal-to-noise ratio (SNR) of a blanker in baseband OFDM-based PLC, which are real-valued and used in some PLC standards. We also analyze the performance of separate real/imaginary blanking, instead of the traditional amplitude blanking, in passband OFDM-based PLC systems. It will be shown that the performance of amplitude blanking is similar to separate real/imaginary blanking in high signal-to-impulsivenoise ratio (SINR) values. However, when the SINR is low, the performance of amplitude blanking is better than the separate real/imaginary blanking. The difference is about 1 dB for SINR = -10 dB and 1.5 dB for SINR = -30 dB when signalto-background-noise ratio, SBNR = 25 dB and impulsive noise probability p = 0.01.

A Comparison Between Orthogonal and Non-Orthogonal Multiple Access in Cooperative Relaying Power Line Communication Systems

Most, if not all, existing studies on power line communication (PLC) systems as well as industrial PLC standards are based on orthogonal multiple access schemes, such as orthogonal frequency-division multiplexing and code-division multiple access. In this paper, we propose non-orthogonal multiple access (NOMA) for decode-and-forward cooperative relaying PLC systems to achieve higher throughput and improve user fairness. To quantitatively characterize the proposed system performance, we also study conventional cooperative relaying (CCR) PLC systems. We evaluate the performance of the two systems in terms of the average capacity. In this respect, accurate analytical expressions for the average capacity are derived and validated with Monte Carlo simulations. The impact of several system parameters, such as the branching, impulsive noise probability, cable lengths, the power allocation coefficients, and input signal-to-noise ratio, is investigated. The results reveal that the performance of the proposed NOMA-PLC scheme is superior compared with that of the CCR-PLC system. It is also shown that the NOMA-PLC system can be more effective in reducing electromagnetic compatibility associated with PLC and that increasing the network branches can considerably degrade the performance. Moreover, optimizing the power allocation coefficients is found to be of utmost importance to maximize the performance of the proposed system.

Quantized Peak-Based Impulsive Noise Blanking in Power-Line Communications

Impulsive-noise (IN) over power-line channels can cause serious performance degradations. As such, many IN mitigation techniques have been proposed in the literature, the most common of which is the blanking technique. The conventional way to implement this technique, however, requires prior knowledge about the IN characteristics to identify the optimal blanking threshold (OBT). When such knowledge cannot be obtained, the performance deteriorates rapidly. To alleviate this, we propose a lookup table (LUT)-based algorithm with uniform quantization to utilize estimates of the peak-to-average power ratio at the receiver to determine the OBT. To fully evaluate the performance of the proposed method, we investigate the impact of quantization bits on the system performance in terms of signal-to-noise ratio (SNR) and symbol error rate under various IN scenarios. The results reveal that a 5-bit LUT is sufficient to achieve a gain of up to 3-dB SNR improvement relative to the conventional blanking method. It will also be shown that to maintain good performance, the resolution of quantization must be increased especially when the IN probability of occurrence is relatively high.

Joint Erasure Marking and Viterbi Decoding Algorithm for Unknown Impulsive Noise Channels

In many real-world communication systems, the extent of non-Gaussian impulsive noise (IN) rather than Gaussian noise poses practical limits on the achievable system performance. The decoding of IN-corrupted signals is complicated by the fact that accurate IN statistics are typically unavailable at the receiver. Without exploiting the IN statistics, the conventional method is to try to mark the IN-corrupted symbols as erasures preceding a Euclidean metric based decoder. In this work, a novel joint erasure marking and Viterbi algorithm (JEVA) is proposed to decode the convolutionally coded data transmitted over an unknown impulsive noise channel. Based on the Bernoulli-Gaussian IN model, it is empirically demonstrated that JEVA not only can offer significant performance improvement over the conventional separate erasure marking and Viterbi decoding method, but also can almost achieve the optimal performance of the maximum likelihood decoder that fully exploits the perfect knowledge of the IN probability density function. Various implementations of JEVA are proposed to provide different performance-complexity trade-offs.

Detail preserving impulsive noise removal

Most image processing applications require noise elimination. For example, in applications where derivative operators are applied, any noise in the image can result in serious errors. Impulsive noise appears as a sprinkle of dark and bright spots. Transmission errors, corrupted pixel elements in the camera sensors, or faulty memory locations can cause impulsive noise. Linear filters fail to suppress impulsive noise. Thus, non-linear filters have been proposed. Windyga's peak-and-valley filter, introduced to remove impulsive noise, identifies noisy pixels and then replaces their values with the minimum or maximum value of their neighbors depending on the noise (dark or bright). Its main disadvantage is that it removes fine image details. In this work, a variation of the peak-and-valley filter is proposed to overcome this problem. It is based on a recursive minimum–maximum method, which replaces the noisy pixel with a value based on neighborhood information. This method preserves constant and edge areas even under high impulsive noise probability. Finally, a comparison study of the peak-and-valley filter, the median filter, and the proposed filter is carried-out using different types of images. The proposed filter outperforms other filters in the noise reduction and the image details preservation. However, it operates slightly slower than the peak-and-valley filter.

Histogram-based fuzzy filter for image restoration

In this paper, we present a novel approach to the restoration of noise-corrupted image, which is particularly effective at removing highly impulsive noise while preserving image details. This is accomplished through a fuzzy smoothing filter constructed from a set of fuzzy membership functions for which the initial parameters are derived in accordance with input histogram. A principle of conservation in histogram potential is incorporated with input statistics to adjust the initial parameters so as to minimize the discrepancy between a reference intensity and the output of defuzzification process. Similar to median filters (MF), the proposed filter has the benefits that it is simple and it assumes no a priori knowledge of specific input image, yet it shows superior performance over conventional filters (including MF) for the full range of impulsive noise probability. Unlike in many neuro-fuzzy or fuzzy-neuro filters where random strategy is employed to choose initial membership functions for subsequent lengthy training, the proposed filter can achieve satisfactory performance without any training.