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Journal ArticleDOI

Performance Evaluation of PLC Under the Combined Effect of Background and Impulsive Noises

TL;DR: The analysis presented in this letter closely predicts the behavior of the PLC system under the combined effect of background and impulsive noises.
Abstract: Power line communication (PLC) is the use of power lines for the purpose of electronic data transmission. The presence of additive noise, namely, background noise and impulsive noise, significantly affects the performance of a PLC system. While the background noise is modeled by Nakagami- $m$ distribution, the impulsive noise is modeled using Middleton class A distribution. In this letter, we study the performance of a PLC system under the combined effect of Nakagami- $m$ background noise and Middleton class A impulsive noise assuming binary phase shift keying signaling. The probability density function of decision variable under the influence of additive noise (sum of background noise and impulsive noise) is derived. We also derive an analytical expression for the average bit error rate of the considered PLC system. The analytical expressions are validated by close matching to the simulation results. The analysis presented in this letter closely predicts the behavior of the PLC system under the combined effect of background and impulsive noises.
Citations
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Journal ArticleDOI
TL;DR: This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) systems by proposing a novel ML detection framework driven by an unsupervised learning approach.
Abstract: This paper aims to devise a generalized maximum likelihood (ML) estimator to robustly detect signals with unknown noise statistics in multiple-input multiple-output (MIMO) systems. In practice, there is little or even no statistical knowledge on the system noise, which in many cases is non-Gaussian, impulsive and not analyzable. Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics. To tackle this issue, we propose a novel ML detection framework to effectively recover the desired signal. Our framework is a fully probabilistic one that can efficiently approximate the unknown noise distribution through a normalizing flow. Importantly, this framework is driven by an unsupervised learning approach, where only the noise samples are required. To reduce the computational complexity, we further present a low-complexity version of the framework, by utilizing an initial estimation to reduce the search space. Simulation results show that our framework outperforms other existing algorithms in terms of bit error rate (BER) in non-analytical noise environments, while it can reach the ML performance bound in analytical noise environments.

53 citations


Additional excerpts

  • ...have investigated the system performance and found the ML detectors in [16]–[18], which is critical for the development of the model-driven methods....

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Journal ArticleDOI
TL;DR: The aim of this brief is to propose a differential chaos shift keying (DCSK) modulation scheme as a potential candidate for smart grid communication networks and prove the advantages of this low-cost noncoherent modulation technique for PLC systems over its rivals.
Abstract: The past few years have witnessed a tremendous development in power-line communications (PLCs) for the realization of smart grids. Since power lines were not originally intended for conveying high-frequency signals, any communication over these lines would be exposed to severe adversarial factors, such as interference, impulsive, and phase noise. This elucidates the importance of employing robust modulation techniques and motivates research in this direction. Indeed, the aim of this brief is to propose a differential chaos shift keying (DCSK) modulation scheme as a potential candidate for smart grid communication networks. This DCSK class of noncoherent modulation is very robust against linear and nonlinear channel distortions. More importantly, the demodulation process can be carried out without any channel estimator at the receiver side. In this work, we analyze the bit error rate performance of DCSK over multipath PLC channels in which phase, background, and impulsive noise are present. A simulator is developed to verify the performance of the proposed DCSK against direct sequence code division multiple access and direct sequence differential phase shift keying. The results presented in this work prove the advantages of this low-cost noncoherent modulation technique for PLC systems over its rivals.

46 citations


Cites background from "Performance Evaluation of PLC Under..."

  • ...Impulse noise has much wider power spectral density than the background and phase noise [5]–[7]....

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  • ...Indeed, it has been experimentally verified in [5]–[7] that the latter follows the Nakagami-m distribution....

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Journal ArticleDOI
TL;DR: This paper comprehensively reviews the progress of several solar PV-based monitoring technologies focusing on various data processing modules and data transmission protocols and offers selective proposals for future research works.
Abstract: Solar photovoltaic (PV) is one of the prominent sustainable energy sources which shares a greater percentage of the energy generated from renewable resources. As the need for solar energy has risen tremendously in the last few decades, monitoring technologies have received considerable attention in relation to performance enhancement. Recently, the solar PV monitoring system has been integrated with a wireless platform that comprises data acquisition from various sensors and nodes through wireless data transmission. However, several issues could affect the performance of solar PV monitoring, such as large data management, signal interference, long-range data transmission, and security. Therefore, this paper comprehensively reviews the progress of several solar PV-based monitoring technologies focusing on various data processing modules and data transmission protocols. Each module and transmission protocol-based monitoring technology is investigated with regard to type, design, implementations, specifications, and limitations. The critical discussion and analysis are carried out with respect to configurations, parameters monitored, software, platform, achievements, and suggestions. Moreover, various key issues and challenges are explored to identify the existing research gaps. Finally, this review delivers selective proposals for future research works. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of the monitoring technologies in future sustainable solar PV applications.

39 citations

Journal ArticleDOI
TL;DR: The cost functions used in these algorithms have been summarized and a timeline of algorithm development in this area has been added to provide an excellent overview on the topic.

31 citations

Journal ArticleDOI
TL;DR: Analysis of a dual-hop wireless-power line mixed communication setup employing a decode-and-forward relay in terms of analytical average bit error rate (BER), outage probability, and average channel capacity finds that the system performance deteriorates as the impulsive noise index and the arrival probability of theImpulsive component of the PLC additive noise increase.
Abstract: Wireless communications and power line communications (PLC) are essential components for smart grid communications. This paper analyses the performance of a dual-hop wireless-power line mixed communication setup employing a decode-and-forward relay in terms of analytical average bit error rate (BER), outage probability, and average channel capacity. The Nakagami- $m$ distribution captures the wireless channel fading; whereas the PLC channel gain is characterized by the Log-normal distribution. The additive PLC channel noise is assumed to be Bernoulli-Gaussian distributed. Approximate closed-form expression of the average BER and exact closed-form expression of the outage probability are derived for the considered system. Further, we obtain an approximate closed-form expression of the capacity of the wireless-power line mixed system in terms of the Meijer-G function. It is observed that the system performance deteriorates as the impulsive noise index and the arrival probability of the impulsive component of the PLC additive noise increase.

27 citations


Cites methods from "Performance Evaluation of PLC Under..."

  • ...Recently in [9], the analytical average BER was evaluated for a PLC system under the combined impact of Nakagami-m distributed background noise and Middleton class A distributed impulsive noise....

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References
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Journal ArticleDOI

872 citations


Additional excerpts

  • ...Then, the distribution of their sum w is given by [19]...

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01 Aug 1977
TL;DR: This study is devoted to the development of analytically tractable, experimentally verifiable, statistical-physical models of man-made and natural electromagnetic interference, whose degrading effects on system performance can be severe.
Abstract: Most man-made and natural electromagnetic interference, or "noise," are highly non-Gaussian random processes, whose degrading effects on system performance can be severe, particularly on most conventional systems, which are designed for optimal or near optimal performance against normal noise. In addition, the nature, origins, measurement, and prediction of the general EM interference environment are a major concern of any adequate spectral management program. Accordingly, this study is devoted to the development of analytically tractable, experimentally verifiable, statistical-physical models of such electromagnetic interference. Here, classification into three major types of noise is made: Class A (narrow band vis-a-vis the receiver), Class B (broad band vis-a-vis the receiver), and Class C (= Class A + Class B). First-order statistical models are constructed for the Class A and Class B cases. In particular, the APD (a posteriori probability distribution) or exceedance probability, PD, vis;P1 (? > ?o)A,B, (and the associated probability densities, pdf's w1(?)A,B,[1]) of the envelope are obtained; (the phase is shown to be uniformly distributed in (0, 2?). These results are canonical, i.e., their analytic forms are invariant of the particular noise source and its quantifying parameter values, levels, etc. Class A interference is described by a 3-parameter model, Class B noise by a 6-parameter model.

807 citations


"Performance Evaluation of PLC Under..." refers methods in this paper

  • ...Although, the Bernoulli-Gaussian distribution is used to represent the impulsive noise in PLC [8] due to the analytical simplicity it offers, but the Middleton class A distribution serves as a basic model for the PLC systems used for obtaining realistic estimates of the system performance for the general spectral-use environment as justified in [9], [10]....

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  • ...We assume that the impulsive noise is modeled using the Middleton class A distribution as justified in [9]–[12]....

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Journal ArticleDOI
TL;DR: In this article, the authors developed analytically tractable, experimentally verifiable, statistical-physical models of electromagnetic interference, which are invariant to the particular noise source and its quantifying parameter values, levels, etc.
Abstract: Most man-made and natural electromagnetic interference, or "noise," are highly non-Gaussian random processes, whose degrading effects on system performance can be severe, particularly on most conventional systems, which are designed for optimal or near optimal performance against normal noise. In addition, the nature, origins, measurement, and prediction of the general EM interference environment are a major concern of any adequate spectral management program. Accordingly, this study is devoted to the development of analytically tractable, experimentally verifiable, statistical-physical models of such electromagnetic interference. Here, classification into three major types of noise is made: Class A (narrow band vis-a-vis the receiver), Class B (broad band vis-a-vis the receiver), and Class C (= Class A + Class B). First-order statistical models are constructed for the Class A and Class B cases. In particular, the APD (a posteriori probability distribution) or exceedance probability, PD, vis;P1 (? > ?o)A,B, (and the associated probability densities, pdf's w1(?)A,B,[1]) of the envelope are obtained; (the phase is shown to be uniformly distributed in (0, 2?). These results are canonical, i.e., their analytic forms are invariant of the particular noise source and its quantifying parameter values, levels, etc. Class A interference is described by a 3-parameter model, Class B noise by a 6-parameter model.

683 citations

Journal ArticleDOI
TL;DR: In this paper, a frequency-domain approach is presented to characterize and model the statistical variation of power-line noise, considering both the background noise and the impulsive noise, and the performance of two major classes of digital modulation schemes, namely single-carrier modulation and multicarrier modulation, are analyzed and compared.
Abstract: Power line noise is known to affect the performance of broadband power-line communications significantly. This paper presents a frequency-domain approach to characterize and model the statistical variation of power-line noise. The model considers both the background noise and the impulsive noise. The background noise model is based on statistical analysis of the results from two long-term measurements of noise spectrum conducted at two separate sites of a laboratory and a residential apartment. On the other hand, the impulsive noise model is obtained by direct measurements from the noise sources (i.e., various electrical household appliances). The amount of impulse noise reaching a power-line communications (PLC) receiver can then be determined with consideration of the channel transfer characteristics between the noise sources and the PLC receiver. Using these noise models, the performance of two major classes of digital modulation schemes, namely single-carrier modulation and multicarrier modulation, are analyzed and compared. It is found that the multicarrier scheme performs better than the single-carrier scheme when subjected to the observed power-line noise with non-Gaussian statistics.

376 citations


"Performance Evaluation of PLC Under..." refers background or methods in this paper

  • ...It is also assumed that the phase of the background noise, θ , is uniformly distributed from −π to π [3], [16], [17]....

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  • ...However, the effect of impulsive noise has been ignored in the BER analysis in [3]–[7]....

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  • ...The BER of single carrier binary phase shift keying (BPSK) signal under the influence of both background noise and impulsive noise has been derived in [8], but the analysis is performed under the assumption that the background noise is additive white Gaussian, which is not applicable to PLC systems [3], [4], [6]....

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  • ...The experimental measurements performed in [3] and [15] support that the amplitude of the background...

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  • ...The long-term measurements of noise spectrum in PLC conducted in [3] over a frequency range of 1 MHz to 30 MHz suggest that time domain amplitude of the background noise follows Nakagami-m distribution....

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Journal ArticleDOI
TL;DR: By using optimum and (locally optimum) detection algorithms (canonically and explicitly derived), substantial savings in signal power and/or spectrum space can be achieved for operation in these highly nonGaussian interference environments.
Abstract: Because communications systems are seldom significantly interfered with by classical white Gaussian noise, it is necessary to consider other, appropriate (and tractable) interference models, if realistic estimates of system performance are to be obtained for the general spectral-use environment. For this purpose, Middleton's recently developed canonical statistical-physical model of "impulsive" interference is applied to real-world communication channels. The principal features of this model are first summarized, including the statistical relations required for the solution of signal detection problems. [Excellent agreement of these model statistics with correspondingly measured statistics is also noted.] The model for narrow-band impulsive interference (Class A noise, a subset of the overall model) is next specifically applied to an important class of coherent signal detection problems. Algorithms for error probabilities in optimum detection are then obtained, along with performance bounds, for the same error probabilities. Since it is known that in order to gain significant improvement over current receivers, the number of (essentially) independent samples of the received interference waveform must be enlarged (i.e., large "processing gains"), the performance results here are given parametrically in the number of samples, or equivalently, in the time-bandwidth product. Performance of current suboptimum receivers is then obtained and compared to the optimum performance. It is shown that very substantial savings in signal power and/or spectrum space can usually be achieved by using the indicated optimal algorithms. Since physical realization of the completely optimum detection algorithms cannot, in general, be economically realized, the somewhat more conservative, corresponding locally optimum Bayes detection (LOBD) receivers are derived. In general, these LOBD structures require adaptive, highly non-linear filters, preceding the conventional correlation detector elements characteristic of optimum receivers for Gaussian interference. Performance for these non-linear, optimum threshold systems is then determined, specifically in Part I for coherent reception.

368 citations


"Performance Evaluation of PLC Under..." refers methods in this paper

  • ...Although, the Bernoulli-Gaussian distribution is used to represent the impulsive noise in PLC [8] due to the analytical simplicity it offers, but the Middleton class A distribution serves as a basic model for the PLC systems used for obtaining realistic estimates of the system performance for the general spectral-use environment as justified in [9], [10]....

    [...]