non-Gaussian Noise Channels Research Articles

On the performance of digital systems in α-[formula omitted] fading and non-Gaussian noise channels

In this paper, a closed-form expression is derived for the bit error probability (BEP) under several digital modulation schemes, considering the channel subject to α-F fading and non-Gaussian noise, characterized by the additive generalized Gaussian noise (AWGGN) distribution. An asymptotic BEP analysis is carried out in this work. A closed-form and unified BEP expression is derived for a wide range of modulation schemes, under α-F fading and AWGGN for the maximal-ratio combining (MRC), equal gain combining (EGC), and selection combining (SC) based receivers. Comments are provided on the special cases encompassed by the α-F fading and AWGGN noise, as well as on the convergence and complexity analysis of the BEP expressions derived. BEP curves are shown for different parameters that characterize the channel and are corroborated by simulations performed with the Monte-Carlo method.

Abating Jamming in Free Space Optical Systems—A Game-Theoretic Solution

Free space optical (FSO) channel provides a significantly large transmission capacity. However, in the presence of random jamming attacks, the efficacy of FSO communication channel can be affected critically. In this paper, the discrete-input continuous-output memoryless channel (DCMC) capacity of a single-input single-output FSO communication system is investigated under the random jamming effects. The ergodic channel capacity analysis is performed over additive non-Gaussian noise channel (jamming noise channel). The derived closed-form expression of DCMC capacity for the considered FSO system and also, the effects of partial-band jamming and broadband jamming over the FSO channel capacity are verified through simulation results. It is observed that the FSO channel capacity is very sensitive to the degree of jamming and the ergodic channel capacity alters crucially even for small change in jamming probability. The derived results are utilized to study the considered network in the presence of a rational jammer whose ability to jam is limited by its battery capacity. The presence of a rational jammer is addressed by modelling the interaction between the FSO transmitter and the jammer as a non-cooperative game. The closed-form expressions of the equilibrium of the proposed game are derived and verified through simulation. The game-theoretic solution provides a performance improvement of 74% in comparison to uniform power allocation.

Effects of magnetic fields on stochastic resonance in Hodgkin-Huxley neuronal network driven by Gaussian noise and non-Gaussian noise.

Stochastic resonance is a remarkable phenomenon that can enhance signal processing by the addition of random noise. However, the effect of magnetic fields on stochastic resonance under channel noises has been inadequately studied. In this paper, the stochastic resonance in Hodgkin-Huxley neuronal network under Gaussian channel noises and non-Gaussian channel noise were studied, and the effects of electromagnetic field stimulation on stochastic resonance were considered. The results indicate that stochastic resonance in neuronal networks can be induced by Gaussian channel noise and non-Gaussian Levy channel noise, and stochastic resonance may occur more easily under Levy channel noise. The resonance amplitude was significantly improved by selecting appropriate parameters of the magnetic field, while, a too strong magnetic field can be detrimental to the resonance amplitude. Magnetic fields may induce the enhancement of the resonance amplitude by increasing the firing frequency and spiking regularity.

ʅ1-Norm Constrained Minimum Eror Entropy Algorithm

This work proposes a linear phase sparse minimum error entropy adaptive filtering algorithm. The linear phase condition is obtained by considering symmetry or anti symmetry condition onto the system coefficients. The proposed work integrates linear constraint based on linear phase of the system and -norm for sparseness into minimum error entropy adaptive algorithm. The proposed -norm linear constrained minimum error entropy criterion ( -CMEE) algorithm makes use of high-order statistics, hence worthy for non-Gaussian channel noise. The experimental results obtained for linear phase sparse system identification in the presence of non-Gaussian channel noise reveal that the proposed algorithm has lower steady state error and higher convergence rate than other existing MEE variants.

Rate-compatible LDPC convolutional codes over non-gaussian noise channel

This paper is aimed to study the characteristics of the underwater acoustic channel with non-Gaussian noise channel. And Gaussian mixture model (GMM) is utilized to fit the background noise over the non-Gaussian noise channel. Furthermore, coding techniques which use a sequence of rate-compatible low-density parity-check (RC-LDPC) convolutional codes with separate rates are constructed based on graph extension method. The performance study of RC-LDPC convolutional codes over non-Gaussian noise channel and the additive white Gaussian noise (AWGN) channel is performed. Study implementation of simulation is that modulation with binary phase shift keying (BPSK), and iterative decoding based on pipeline log-likelihood rate belief propagation (LLRBP) algorithm. Finally, it is shown that RC-LDPC convolutional codes have good bit-rate-error (BER) performance and can effectively reduce the impact of noise.

Brain Inspired Dynamic System for the Quality of Service Control over the Long-Haul Nonlinear Fiber-Optic Link.

Brain-inspired intelligence using the cognitive dynamic system (CDS) concept is proposed to control the quality-of-service (QoS) over a long-haul fiber-optic link that is nonlinear and with non-Gaussian channel noise. Digital techniques such as digital-back-propagation (DBP) assume that the fiber optic link parameters, such as loss, dispersion, and nonlinear coefficients, are known at the receiver. However, the proposed CDS does not need to know about the fiber optic link physical parameters, and it can improve the bit error rate (BER) or enhance the data rate based on information extracted from the fiber optic link. The information extraction (Bayesian statistical modeling) using intelligent perception processing on the received data, or using the previously extracted models in the model library, is carried out to estimate the transmitted data in the receiver. Then, the BER is sent to the executive through the main feedback channel and the executive produces actions on the physical system/signal to ensure that the BER is continuously under the forward-error-correction (FEC) threshold. Therefore, the proposed CDS is an intelligent and adaptive system that can mitigate disturbance in the fiber optic link (especially in an optical network) using prediction in the perceptor and/or doing proper actions in the executive based on BER and the internal reward. A simplified CDS was implemented for nonlinear fiber optic systems based on orthogonal frequency division multiplexing (OFDM) to show how the proposed CDS can bring noticeable improvement in the system’s performance. As a result, enhancement of the data rate by 12.5% and the Q-factor improvement of 2.74 dB were achieved in comparison to the conventional system (i.e., the system without smart brain).

A new and fast correntropy-based method for system identification with exemplifications in low-SNR communications regime

One of the most significant issues in machine learning is system identification with many applications, e.g., channel estimation (CE) in digital communications. Introducing a new correntropy-based method, this paper deals with the comparison between mean square error (MSE) and information theoretic measures in non-Gaussian noise channel estimation, by analyzing the MSE, minimum error entropy (MEE) and correntropy algorithms in several channel models utilizing neural networks. The first contribution of this paper is introducing a new correntropy-based conjugate gradient (CCG) method and applying it in the CE problem, which this new algorithm converges faster than standard maximum correntropy criterion algorithm. Aiming at this contribution, the better convergence rate is discussed analytically and it is proved that the CCG could converge to the optimal solution quadratically. Next, the performance of an extended MSE algorithm is compared with information theoretic criteria; in addition, a comparison between MEE and correntropy-based algorithm is presented. The Monte Carlo results illustrate that correntropy and MEE outperform MSE algorithm in low-SNR communications especially in the presence of impulsive noise. Then, we apply the trained neural networks in the receiver as an equalizer to obtain the intended performance for different SNR values.

Multicarrier Downlink Transmission for High-speed Railway in Non-Gaussian Noise Channels

We consider a wireless wideband downlink transmission for high-speed railway. In this scenario, the wireless channel is double selective, i.e., multipath and highly time-varying, and involves non-Gaussian noise at the receiver side. Conventional receiver algorithms designed for Gaussian noise and time-invariant channels are inefficient especially for impulsive noise and time-varying multipath channels, which are the main challenges in high-speed railway. They may lead to communication outage and involve large carrier offsets, respectively. Regarding the challenges, a synthesis scheme is proposed to resolve these issues in this paper. First, in order to mitigate the impulsive noise, three impulsive noise detection algorithms are adopted to discriminate impulsive noise from the received signal. The blanking algorithm is used to suppress the discriminated noise. Then, the refined signal goes through a well-designed filtering, which transforms the received signal with varying frequency offsets into an angle domain. The advantage in the angle domain is that the frequency offset in each angle becomes nearly constant. Thus, the frequency offsets can be estimated within each angle to realize accurate estimation. In the simulation, three different impulsive noise models are considered to evaluate the communication reliability in this circumstance. The simulation results show that the proposed scheme significantly improves the reliable performance of the high-speed railway communication systems.

Robust Hierarchical Data Fusion Scheme for Large-Scale Sensor Network

The advanced driver assistant system (ADAS) requires the collection of a large amount of information including road conditions, environment, vehicle status, condition of the driver, and other useful data. In this regard, large-scale sensor networks can be an appropriate solution since they have been designed for this purpose. Recent advances in sensor network technology have enabled the management and monitoring of large-scale tasks such as the monitoring of road surface temperature on a highway. In this paper, we consider the estimation and fusion problems of the large-scale sensor networks used in the ADAS. Hierarchical fusion architecture is proposed for an arbitrary topology of the large-scale sensor network. A robust cluster estimator is proposed to achieve robustness of the network against outliers or failure of sensors. Lastly, a robust hierarchical data fusion scheme is proposed for the communication channel between the clusters and fusion center, considering the non-Gaussian channel noise, which is typical in communication systems.

The Dispersion of Nearest-Neighbor Decoding for Additive Non-Gaussian Channels

We study the second-order asymptotics of information transmission using random Gaussian codebooks and nearest neighbor decoding over a power-limited stationary memoryless additive non-Gaussian noise channel. We show that the dispersion term depends on the non-Gaussian noise only through its second and fourth moments, thus complementing the capacity result (Lapidoth, 1996), which depends only on the second moment. Furthermore, we characterize the second-order asymptotics of point-to-point codes over $K$ -sender interference networks with non-Gaussian additive noise. Specifically, we assume that each user’s codebook is Gaussian and that NN decoding is employed, i.e., that interference from the $K-1$ unintended users (Gaussian interfering signals) is treated as noise at each decoder. We show that while the first-order term in the asymptotic expansion of the maximum number of messages depends on the power of the interfering codewords only through their sum, this does not hold for the second-order term.

Robust Signal-to-Noise Ratio Estimation in Non-Gaussian Noise Channel

Signal-to-noise ratio (SNR) estimation available in the literature are designed based on the assumption of Gaussian noise models. These estimators may produce misleading results when the distribution of the noise deviates from Gaussian. This paper investigates the performance of existing SNR estimators in an additive non-Gaussian noise channel based on a Gaussian mixture model. An expectation---maximization (EM) based approach is proposed for optimum SNR estimation in the non-Gaussian noise channel. In addition, the Cramer---Rao bound is derived and used as a benchmark to assess the performance of the SNR estimators. Simulation results confirm the optimality and robustness of the proposed EM-based estimator in Gaussian and non-Gaussian noise channels.

Extension of de Bruijn's identity to dependent non-Gaussian noise channels

Abstract De Bruijn's identity relates two important concepts in information theory: Fisher information and differential entropy. Unlike the common practice in the literature, in this paper we consider general additive non-Gaussian noise channels where more realistically, the input signal and additive noise are not independently distributed. It is shown that, for general dependent signal and noise, the first derivative of the differential entropy is directly related to the conditional mean estimate of the input. Then, by using Gaussian and Farlie–Gumbel–Morgenstern copulas, special versions of the result are given in the respective case of additive normally distributed noise. The previous result on independent Gaussian noise channels is included as a special case. Illustrative examples are also provided.

Robust correntropy ICA based blind channel estimation for MIMO-OFDM systems

Purpose – The purpose of this paper is to propose a robust correntropy assisted blind channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) for improved channel gains estimation and channel ordering and sign ambiguities resolution in non-Gaussian noise channel. Design/methodology/approach – The correntropy independent component analysis with L1-norm cost function is used for blind channel estimation. Then a correntropy-based method is formulated to resolve the sign and order ambiguities of the channel estimates. Findings – Simulation study on Gaussian noise scenario shows that the proposed method achieves almost the same performance as the conventional L2-norm based method. However, in non-Gaussian noise scenarios performance of the proposed method significantly outperforms the conventional and other popular estimators in terms of mean square error (MSE). To solve the ordering and sign ambiguities problems, an auto-correntropy-based method is proposed and compared with the extended cross-correlation-based method. Simulation study shows improved performance of the proposed method in terms of MSE. Originality/value – This paper presents for the first time, a correntropy-based blind channel estimator for MIMO-OFDM as well as simulated comparison results with traditional correlation-based methods in non-Gaussian noise environment.

Optimal signaling and detector design for M-ary communication systems in the presence of multiple additive noise channels

An M-ary communication system is considered in which the transmitter and the receiver are connected via multiple additive (possibly non-Gaussian) noise channels, any one of which can be utilized for the transmission of a given symbol. Contrary to deterministic signaling (i.e., employing a fixed constellation), a stochastic signaling approach is adopted by treating the signal values transmitted for each information symbol over each channel as random variables. In particular, the joint optimization of the channel switching (i.e., time sharing among different channels) strategy, stochastic signals, and decision rules at the receiver is performed in order to minimize the average probability of error under an average transmit power constraint. It is proved that the solution to this problem involves either one of the following: (i) deterministic signaling over a single channel, (ii) randomizing (time sharing) between two different signal constellations over a single channel, or (iii) switching (time sharing) between two channels with deterministic signaling over each channel. For all cases, the optimal strategies are shown to employ corresponding maximum a posteriori probability (MAP) decision rules at the receiver. In addition, sufficient conditions are derived in order to specify whether the proposed strategy can or cannot improve the error performance over the conventional approach, in which a single channel is employed with deterministic signaling at the average power limit. Finally, numerical examples are presented to illustrate the theoretical results.

EFFECT OF NON-GAUSSIAN CHANNEL NOISE ON COHERENCE RESONANCE IN COUPLED STOCHASTIC HODGKIN–HUXLEY NEURONS

In this paper, we study the effect of non-Gaussian channel noise (NCN) on the spiking coherence of a single and an array of bi-directionally coupled Hodgkin–Huxley neurons, mainly investigating how the non-Gaussian character of channel noise affects the coherence resonance (CR) induced by channel noise and by the number of neurons. It is found that, when NCN's deviation q from Gaussian distribution is increased, the CR moves to larger patch area (smaller channel noise) and smaller neuron number, which means that CR occurs in bigger ion channel clusters and smaller neuron number when q is increased. This result shows that, depending on the type of NCN, CR occurs in different sizes of ion channel clusters and numbers of neurons. The underlying mechanism is briefly discussed in terms of the property of NCN. These findings may help to better understand the roles of NCN for improving the time precision of the information processing in coupled stochastic neurons.

CMA Blind Multiuser Detector Based on FLOS in Impulse Noise

Blind multiuser detector can suppress the MAI(multiple address interference) effectively. Gaussian channel noise is assumed in the traditional methods, but the non-Gaussian channel noise is more realistic. This paper proposes a new CMA(Constant Modulus Algorithm) criterion employing FLOS(fractional lower-order statistic). Theoretical analyses and the computer simulations indicate that the associated FLOS-CMA blind MUD(MultiUser Detection) method,based on a stochastic gradient descent algorithm has a good performance in BER(bit error rate). The traditional MUD algorithm is the special case of this algorithm.

Space-alternating generalised expectation–maximisation-based H-infinity channel estimator for multiple-input multiple-output-orthogonal frequency division multiplexing systems

This study presents a low-complexity and robust H-infinity channel estimator for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The H-infinity estimator, which has never been devoted to MIMO-OFDM systems, could be implemented by applying a multiple-order auto-regression (AR) model. However, it may increase the design complexity of receivers and lead to poor real-time property when this model is used for MIMO-OFDM systems, which makes the authors abandon AR model. In order to reduce the number of matrices manipulations because of the each received OFDM symbols from different transmit antennas, the iterative space-alternating generalised expectation–maximisation (SAGE) algorithm is adopted. Furthermore, to deal with the effect of non-Gaussian noise (NGN) channels, because of various natural or man-made impulsive sources, an equivalent signal model (ESM) is introduced to alleviate the effect of this issue and enhance the robust of SAGE-based H-infinity estimator. Simulation results show that H-infinity estimator has almost the same bit error rate performance as optimal maximum a posteriori estimator. The performance gain afforded by using ESM can be substantial when compared with using the traditional signal model, which dramatically enhance the robustness of SAGE-based H-infinity estimator against NGN channels.

Robust estimators for frequency offset of OFDM in non-Gaussian noise

This paper addresses robust estimation for the frequency offset of orthogonal frequency division multiplexing in non-Gaussian noise channels. We first propose a maximum-likelihood (ML) estimator in non-Gaussian noise modeled as a complex isotropic Cauchy process, and then present a simpler, yet still robust, estimator based on the ML estimator. From numerical results, it is confirmed that the proposed estimators offer a substantial performance improvement over the conventional estimators in non-Gaussian noise channels.

Fast and Robust Modulation Classification via Kolmogorov-Smirnov Test

A new approach to modulation classification based on the Kolmogorov-Smirnov (K-S) test is proposed. The K-S test is a non-parametric method to measure the goodness of fit. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic derived from the received signal, and comparing it with the CDFs or the ECDFs of the signal under each candidate modulation format. The K-S-based modulation classifiers are developed for various channels, including the AWGN channel, the flat-fading channel, the OFDM channel, and the channel with unknown phase and frequency offsets, as well as the non-Gaussian noise channel, for both QAM and PSK modulations. Extensive simulation results demonstrate that compared with the traditional cumulant-based classifiers, the proposed K-S classifiers offer superior classification performance, require less number of signal samples (thus is fast), and is more robust to various channel impairments.

Continuous-variable entanglement distillation of non-Gaussian mixed states

Many different quantum information communication protocols such as teleportation, dense coding and entanglement based quantum key distribution are based on the faithful transmission of entanglement between distant location in an optical network. The distribution of entanglement in such a network is however hampered by loss and noise that is inherent in all practical quantum channels. Thus, to enable faithful transmission one must resort to the protocol of entanglement distillation. In this paper we present a detailed theoretical analysis and an experimental realization of continuous variable entanglement distillation in a channel that is inflicted by different kinds of non-Gaussian noise. The continuous variable entangled states are generated by exploiting the third order non-linearity in optical fibers, and the states are sent through a free-space laboratory channel in which the losses are altered to simulate a free-space atmospheric channel with varying losses. We use linear optical components, homodyne measurements and classical communication to distill the entanglement, and we find that by using this method the entanglement can be probabilistically increased for some specific non-Gaussian noise channels.