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

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.

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.

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.

Abstract: Communications over power lines in the frequency range above 2 MHz, commonly referred to as broadband (BB) power line communications (PLC), has been the focus of increasing research attention and standardization efforts in recent years. BB-PLC channels are characterized by a dominant colored non-Gaussian additive noise, as well as by periodic variations of the channel impulse response and of the noise statistics. In this paper, we study the fundamental rate limits for BB-PLC channels by bounding their capacity while accounting for the unique properties of these channels. We obtain explicit expressions for the derived bounds for several BB-PLC noise models, and illustrate the resulting fundamental limits in a numerical analysis.

Abstract: 0 Introduction 1 Elementary Functions 2 Indefinite Integrals of Elementary Functions 3 Definite Integrals of Elementary Functions 4.Combinations involving trigonometric and hyperbolic functions and power 5 Indefinite Integrals of Special Functions 6 Definite Integrals of Special Functions 7.Associated Legendre Functions 8 Special Functions 9 Hypergeometric Functions 10 Vector Field Theory 11 Algebraic Inequalities 12 Integral Inequalities 13 Matrices and related results 14 Determinants 15 Norms 16 Ordinary differential equations 17 Fourier, Laplace, and Mellin Transforms 18 The z-transform

Abstract: Part 1 Probability and Random Variables 1 The Meaning of Probability 2 The Axioms of Probability 3 Repeated Trials 4 The Concept of a Random Variable 5 Functions of One Random Variable 6 Two Random Variables 7 Sequences of Random Variables 8 Statistics Part 2 Stochastic Processes 9 General Concepts 10 Random Walk and Other Applications 11 Spectral Representation 12 Spectral Estimation 13 Mean Square Estimation 14 Entropy 15 Markov Chains 16 Markov Processes and Queueing Theory

Abstract: Part 1 Probability and Random Variables 1 The Meaning of Probability 2 The Axioms of Probability 3 Repeated Trials 4 The Concept of a Random Variable 5 Functions of One Random Variable 6 Two Random Variables 7 Sequences of Random Variables 8 Statistics Part 2 Stochastic Processes 9 General Concepts 10 Random Walk and Other Applications 11 Spectral Representation 12 Spectral Estimation 13 Mean Square Estimation 14 Entropy 15 Markov Chains 16 Markov Processes and Queueing Theory

Abstract: (1965). Handbook of Mathematical Functions with Formulas. Technometrics: Vol. 7, No. 1, pp. 78-79.