Robust Cyclic MUSIC Algorithm for Finding Directions in Impulsive Noise Environment

This paper addresses the issue of direction finding of a cyclostationary signal under impulsive noise environments modeled by α-stable distribution. Since α-stable distribution does not have finite second-order statistics, the conventional cyclic correlation-based signal-selective direction finding algorithms do not work effectively. To resolve this problem, we define two robust cyclic correlation functions which are derived from robust statistics property of the correntropy and the nonlinear transformation, respectively. The MUSIC algorithm with the robust cyclic correlation matrix of the received signals of arrays is then used to estimate the direction of cyclostationary signal in the presence of impulsive noise. The computer simulation results demonstrate that the two proposed robust cyclic correlation-based algorithms outperform the conventional cyclic correlation and the fractional lower order cyclic correlation based methods

Modeling and Mitigation of Wireless Communications Interference for Spectrum Sharing with Radar

Due to both economic incentives and policy mandates, researchers increasingly face the challenge of enabling spectrum sharing between radar and wireless communications systems. In the past eight years, researchers have begun to suggest a wide variety of approaches to radar-communications spectrum sharing, ranging from transmitter design to receiver design, from spatial to temporal to other-dimensional multiplexing, and from cooperative to non-cooperative sharing. Within this diverse field of innovation, this dissertation makes two primary contributions. First, a model for wireless communications interference and its effects on adaptive-threshold radar detection is proposed. Based on both theoretical and empirical study, we find evidence for both Gaussian and non-Gaussian communications interference models, depending on the modeling situation. Further, such interference can impact radar receivers via two mechanisms—model mismatch and boost to the underlying noise floor—and both mechanisms deserve attention. Second, an innovative signal processing algorithm is proposed for radar detection in the presence of cyclostationary, linearly-modulated, digital communications (LMDC) interference (such as OFDM or CDMA) and a stationary background component. The proposed detector consists of a novel whitening filter followed by the traditional matched filter. Performance results indicate that the proposed cyclostationary-based detector outperforms a standard equivalent detector based on a stationary interference model, particularly when the number of cyclostationary LMDC transmitters is small and their interference-to-noise ratio (INR) is large relative to the stationary background

Characterization and modeling of the channel and noise for broadband indoor Power Line Communication (PLC) networks.

Doctor of Philosophy in Electronic Engineering. University of KwaZulu-Natal, Durban 2016Power Line Communication (PLC) is an interesting approach in establishing last mile broadband access especially in rural areas. PLC provides an already existing medium for broadband internet connectivity as well as monitoring and control functions for both industrial and indoor usage. PLC network is the most ubiquitous network in the world reaching every home. However, it presents a channel that is inherently hostile in nature when used for communication purposes. This hostility is due to the many problematic characteristics of the PLC from a data communications’ perspective. They include multipath propagation due to multiple reflections resulting from impedance mismatches and cable joints, as well as the various types of noise inherent in the channel. Apart from wireless technologies, current high data rate services such as high speed internet are provided through optical fibre links, Ethernet, and VDSL (very-high-bit-rate digital subscriber line) technology. The deployment of a wired network is costly and demands physical effort. The transmission of high frequency signals over power lines, known as power line communications (PLC), plays an important role in contributing towards global goals for broadband services inside the home and office. In this thesis we aim to contribute to this ideal by presenting a powerline channel modeling approach which describes a powerline network as a lattice structure. In a lattice structure, a signal propagates from one end into a network of boundaries (branches) through numerous paths characterized by different reflection/transmission properties. Due to theoretically infinite number of reflections likely to be experienced by a propagating wave, we determine the optimum number of paths required for meaningful contribution towards the overall signal level at the receiver. The propagation parameters are obtained through measurements and other model parameters are derived from deterministic power system. It is observed that the notch positions in the transfer characteristics are associated with the branch lengths in the network. Short branches will result in fewer notches in a fixed bandwidth as compared to longer branches. Generally, the channel attenuation increase with network size in terms of number of branches. The proposed model compares well with experimental data. This work presents another alternative approach to model the transfer characteristics of power lines for broadband power line communication. The model is developed by considering the power line to be a two-wire transmission line and the theory of transverse electromagnetic (TEM) wave propagation. The characteristic impedance and attenuation constant of the power line are determined through measurements. These parameters are used in model simplification and determination of other model parameters for typical indoor multi-tapped transmission line system. The transfer function of the PLC channel is determined by considering the branching sections as parallel resonant circuits (PRC) attached to the main line. The model is evaluated through comparison with measured transfer characteristics of known topologies and it is in good agreement with measurements. Apart from the harsh topology of power line networks, the presence of electrical appliances further aggravates the channel conditions by injecting various types of noises into the system. This thesis also discusses the process of estimating powerline communication (PLC) asynchronous impulsive noise volatility by studying the conditional variance of the noise time series residuals. In our approach, we use the Generalized Autoregressive Conditional Heteroskedastic (GARCH) models on the basis that in our observations, the noise time series residuals indicate heteroskedasticity. By performing an ordinary least squares (OLS) regression of the noise data, the empirical results show that the conditional variance process is highly persistent in the residuals. The variance of the error terms are not uniform, in fact, the error terms are larger at some portions of the data than at other time instances. Thus, PLC impulsive noise often exhibit volatility clustering where the noise time series is comprised of periods of high volatility followed by periods of high volatility and periods of low volatility followed by periods of low volatility. The burstiness of PLC impulsive noise is therefore not spread randomly across the time period, but instead has a degree of autocorrelation. This provides evidence of time-varying conditional second order moment of the noise time series. Based on these properties, the noise time series data is said to suffer from heteroskedasticity. GARCH models addresses the deficiencies of common regression models such as Autoregressive Moving Average (ARMA) which models the conditional expectation of a process given the past, but regards the past conditional variances to be constant. In our approach, we predict the time-varying volatility by using past time-varying variances in the error terms of the noise data series. Subsequent variances are predicted as a weighted average of past squared residuals with declining weights that never completely diminish. The parameter estimates of the model indicates a high degree of persistence in conditional volatility of impulsive noise which is a strong evidence of explosive volatility. Parameter estimation of linear regression models usually employs least squares (LS) and maximum likelihood (ML) estimators. While maximum likelihood remains one of the best estimators within the classical statistics paradigm to date, it is highly reliant on the assumption about the joint probability distribution of the data for optimal results. In our work, we use the Generalized Method of Moments (GMM) to address the deficiencies of LS/ML in order to estimate the underlying data generating process (DGP). We use GMM as a statistical technique that incorporate observed noise data with the information in population moment conditions to determine estimates of unknown parameters of the underlying model. Periodic impulsive noise (short-term) has been measured, deseasonalized and modeled using GMM. The numerical results show that the model captures the noise process accurately. Usually, the impulsive signals originates from connected loads in an electrical power network can often be characterized as cyclostationary processes. A cyclostationary process is described as a non-stationary process whose statistics exhibit periodic time variation, and therefore can be described by virtue of its periodic order. The focus of this chapter centres on the utilization of cyclic spectral analysis technique for identification and analysis of the second-order periodicity (SOP) of time sequences like those which are generated by electrical loads connected in the vicinity of a power line communications receiver. Analysis of cyclic spectrum generally incorporates determining the random features besides the periodicity of impulsive noise, through the determination of the spectral correlation density (SCD). Its effectiveness on identifying and analysing cyclostationary noise is substantiated in this work by processing data collected at indoor low voltage sites

Characterization and modelling of the channel and noise for broadband indoor powerline communication (plc.) networks.

Masters degree. University of KwaZulu-Natal, Durban.Power Line Communication (PLC) is an interesting approach in establishing last mile broad band access especially in rural areas. PLC provides an already existing medium for broad band internet connectivity as well as monitoring and control functions for both industrial and indoor usage. PLC network is the most ubiquitous network in the world reaching every home. However, it presents a channel that is inherently hostile in nature when used for communication purposes. This hostility is due to the many problematic characteristics of the PLC from a data communications’ perspective. They include multipath propagation due to multiple reflections resulting from impedance mismatches and cable joints, as well as the various types of noise inherent in the channel. Apart from wireless technologies, current high data rate services such as high speed internet are provided through optical fibre links, Ethernet, and VDSL (very-high-bit-rate digital subscriber line) technology. The deployment of a wired network is costly and demands physical effort. The transmission of high frequency signals over power lines, known as power line communications (PLC), plays an important role in contributing towards global goals for broadband services inside the home and office. In this thesis we aim to contribute to this ideal by presenting a powerline channel modeling approach which describes a powerline network as a lattice structure. In a lattice structure, a signal propagates from one end into a network of boundaries (branches) through numerous paths characterized by different reflection/transmission properties. Due to theoretically infi nite number of reflections likely to be experienced by a propagating wave, we determine the optimum number of paths required for meaningful contribution towards the overall signal level at the receiver. The propagation parameters are obtained through measurements and other model parameters are derived from deterministic power system. It is observed that the notch positions in the transfer characteristics are associated with the branch lengths in the network. Short branches will result in fewer notches in a fixed bandwidth as compared to longer branches. Generally, the channel attenuation increase with network size in terms of number of branches. The proposed model compares well with experimental data. This work presents another alternative approach to model the transfer characteristics of power lines for broadband power line communication. The model is developed by considering the power line to be a two-wire transmission line and the theory of transverse electromagnetic (TEM) wave propagation. The characteristic impedance and attenuation constant of the power line v are determined through measurements. These parameters are used in model simplification and determination of other model parameters for typical indoor multi-tapped transmission line system. The transfer function of the PLC channel is determined by considering the branching sections as parallel resonant circuits (PRC) attached to the main line. The model is evaluated through comparison with measured transfer characteristics of known topologies and it is in good agreement with measurements. Apart from the harsh topology of power line networks, the presence of electrical appliances further aggravates the channel conditions by injecting various types of noises into the system. This thesis also discusses the process of estimating powerline communication (PLC) asynchronous impulsive noise volatility by studying the conditional variance of the noise time series residuals. In our approach, we use the Generalized Autoregressive Conditional Heteroskedastic (GARCH) models on the basis that in our observations, the noise time series residuals indicate heteroskedasticity. By per forming an ordinary least squares (OLS) regression of the noise data, the empirical results show that the conditional variance process is highly persistent in the residuals. The variance of the error terms are not uniform, in fact, the error terms are larger at some portions of the data than at other time instances. Thus, PLC impulsive noise often exhibit volatility clustering where the noise time series is comprised of periods of high volatility followed by periods of high volatility and periods of low volatility followed by periods of low volatility. The burstiness of PLC impulsive noise is therefore not spread randomly across the time period, but instead has a degree of autocorrelation. This provides evidence of time-varying conditional second order moment of the noise time series. Based on these properties, the noise time series data is said to suffer from heteroskedasticity. GARCH models addresses the deficiencies of common regression models such as Autoregressive Moving Average (ARMA) which models the conditional expectation of a process given the past, but regards the past conditional variances to be constant. In our approach, we predict the time-varying volatility by using past time-varying variances in the error terms of the noise data series. Subsequent variances are predicted as a weighted average of past squared residuals with declining weights that never completely diminish. The parameter estimates of the model indicates a high de gree of persistence in conditional volatility of impulsive noise which is a strong evidence of explosive volatility. Parameter estimation of linear regression models usually employs least squares (LS) and maximum likelihood (ML) estimators. While maximum likelihood remains one of the best estimators within the classical statistics paradigm to date, it is highly reliant vi on the assumption about the joint probability distribution of the data for optimal results. In our work, we use the Generalized Method of Moments (GMM) to address the deficien cies of LS/ML in order to estimate the underlying data generating process (DGP). We use GMM as a statistical technique that incorporate observed noise data with the information in population moment conditions to determine estimates of unknown parameters of the under lying model. Periodic impulsive noise (short-term) has been measured, deseasonalized and modeled using GMM. The numerical results show that the model captures the noise process accurately. Usually, the impulsive signals originates from connected loads in an electrical power network can often be characterized as cyclostationary processes. A cyclostationary process is described as a non-stationary process whose statistics exhibit periodic time varia tion, and therefore can be described by virtue of its periodic order. The focus of this chapter centres on the utilization of cyclic spectral analysis technique for identification and analysis of the second-order periodicity (SOP) of time sequences like those which are generated by electrical loads connected in the vicinity of a power line communications receiver. Analysis of cyclic spectrum generally incorporates determining the random features besides the pe riodicity of impulsive noise, through the determination of the spectral correlation density (SCD). Its effectiveness on identifying and analysing cyclostationary noise is substantiated in this work by processing data collected at indoor low voltage sites

Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

A flexible statistical framework for the characterization and modelling of noise in powerline communication channels.

Doctor of Philosophy in Electronic Engineering.One communication medium that has received a lot of interest in recent years is the power line channel, especially for the delivery of broadband content. This channel has been traditionally used to carry electrical power only. But with the recent advancements in digital signal processing, it is now possible to realize communications through the power grid, both in narrowband and broadband. The use of the power line network for telecommunication purposes constitutes what is referred to as powerline carrier communications or simply powerline communications (PLC). The biggest incentive for PLC technology use is the fact that the power line network is already in place, which greatly reduces the communication network set up cost, since no new cabling layout is required. PLC technology is widely applied in home networking, broadband internet provision and smart grid solutions. However, the PLC channel presents a very hostile communication environment. And as such, no consideration has been made in the design of traditional power line network to accommodate communication services. Of all the PLC channel impairments which include frequency-dependent attenuation, frequency selectivity, multipath and noise, noise is the biggest threat to communication signals. This noise manifests itself in form of coloured background noise, narrowband interference and impulsive noise. A thorough understanding of this noise distribution is therefore crucial for the design of a reliable and high performing PLC system. A proper understanding of the noise characteristics in the PLC channel can only be realized through noise measurements in live power networks, and then analyzing and modeling the noise appropriately. Moreover, the noise scenario in power line networks is very complex and therefore cannot be modeled through mere analytical methods. Additionally, most of the models that have been proposed for the PLC noise previously are mere adaptations of the measured noise to some existing impulsive noise models. These earlier modeling approaches are also rigid and model the noise via a fixed set of parameters. In the introductory work in this thesis, a study of orthogonal frequency division multiplexing (OFDM) as the modulation of choice for PLC systems is presented. A thorough survey of the salient features of this modulation scheme that make it the perfect candidate for PLC modulation needs is presented. In the end, a performance analysis study on the impact of impulsive noise on an OFDM based binary phase shift keying (BPSK) system is done. This study differs from earlier ones in that its focus is on how the elementary parameters that define the impulsive noise affect the system, a departure from the usual norm of considering the overall noise distribution. This study focuses on the impact of interarrival times (IAT), pulse amplitudes as well as pulse widths, among other parameters. In the first part of the main work in this thesis, results of an intensive noise measurement campaign for indoor low voltage power line noise carried out in various power line networks, in the Department of Electrical, Electronic and Computer Engineering buildings at the University of KwaZulu-Natal, Howard campus are presented. The noise measurements are carried out in both time and frequency domains. Next, the noise measurements are then analyzed and modeled using two very flexible data modeling tools; nonparametric kernel density estimators and parametric alpha stable (α-stable) distributions. The kernel method’s ability to overcome all the shortcomings of the primitive histogram method makes it very attractive. In this method, the noise data structure is derived straight from the data itself, with no prior assumptions or restrictions on the data structure, thus effectively overcoming the rigidity associated with previous noise models for power line channels. As such, it results in density estimates that “hug” the measured density as much as possible. The models obtained using the kernel methods are therefore better than any parametric equivalent; something that can always be proven through goodness of fit tests. These models therefore form an excellent reference for parametric modeling of the power line noise. This work forms the author’s first main contribution to PLC research. As a demonstration of the kernel models suitability to act as a reference, parametric models of the noise distribution using the alpha stable (α-stable) distribution are also developed. This distribution is chosen due to its flexibility and ability to capture impulsiveness (long-tailed behaviour), such as the one found in power line noise. Stable distributions are characterized by long/fat tails than those of the Gaussian distribution, and that is the main reason why they are preferable here since the noise characteritics obtained in the kernel technique show visible long/heavy tailed behavior. A parameter estimation technique that is based on quantiles and another on the empirical characteristic function are employed in the extraction of the four parameters that define the characteristic function of the α-stable distribution. The application of the α-stable distribution in other signal processing problems has often been over-simplied by considering the symmetric alpha stable distribution, but in this thesis, the general α-stable distribution is used to model the power line noise. This is necessary so as to ensure that no features of the noise distribution are missed. All the models obtained are validated through error analysis and Chi-square fitness tests. This work forms the author’s second main contribution to PLC research. The author’s last contribution in this thesis is the development of an algorithm for the synthesis of the power line as a Levy stable stochastic process. The algorithm developed is then used to generate the PLC noise process for a random number of alpha stable noise samples using the alpha stable noise parameters obtained in the parametric modeling using stable distributions. This algorithm is generalized for all admissible values of alpha stable noise parameters and therefore results for a Levy stable Gaussian process are also presented for the same number of random noise samples for comparison purposes

Power line communication (PLC) channel measurements and characterization.

M. Sc. Eng. University of KwaZulu-Natal, Durban 2014.The potential of the power line to transport both power and communication signals simultaneously has been realized and practiced for over a century, dating back to the 1900’s. Since the key aspect of power line communications being its expansivity, its implementations were largely as a retrofit technology. This motivation of power line communication is typical for low-, medium-, and high voltage distribution networks. Beyond the “last mile” part, there’s an uprising appeal for intra-building networks currently targeted for home automation (smart homes/buildings) and in-building networking. The optimum use of the existing power line channels has been a focus area for researchers and designers, with the inherent channel hostility proving a serious drawback for high speed data communications. The low-voltage electrical network has unpredictable noise sources, moreover it has two other main disadvantages as a communication channel. The first short coming has to do with the unknown characteristics of the power cable and topology of the network, the second arises from the time-dependent fluctuation of the impedance level of the power line as the loads are switched into and out of the power line network in an unpredictable manner. These factors determine the behaviour of the power line channel when a high frequency signal is impressed on it. This study has shown that the behaviour of indoor power line channels can be captured using a multipath based model even with limited qualitative and/or quantitative knowledge of the network topology. This model is suitable for typical indoor power line channels where knowledge of the topology is near impossible. Some of the feed parameters are obtained through measurements. With sufficient adjustment of control parameters, this model was successfully validated using sample measured channels from the numerous measurements. Through noise measurements, this study has established that impulsive noise is the rifest in the frequency band of interest. The impulsive energy rises well above background noise, which translates to possible data “black outs”. The statistics of the components of this noise are presented. A model of sufficient simplicity is used to facilitate the qualitative description of the background noise through its power spectral density. Two descriptions are provided in terms of the worst and best case scenarios of the background noise occurrences. The model has a good macroscopic capture of the noise power spectral density, with narrow-band interference visible for the worst case noise. Due to the multipath nature of the power line channel, this study also presents the dispersive characteristics of the power line as a communication channel. The power delay profile is used to determine parameters such as first arrival delay, mean excess delay, root mean square delay spread and maximum delay spread. The statistics of these parameters are presented. Also, the coherence bandwidth of power line channels is studied and its relationship with the rms delay spread is developed. It is in view of this work that further research in power line communication and related topics shall be inspired

Detecting and locating electronic devices using their unintended electromagnetic emissions

Electronically-initiated explosives can have unintended electromagnetic emissions which propagate through walls and sealed containers. These emissions, if properly characterized, enable the prompt and accurate detection of explosive threats. The following dissertation develops and evaluates techniques for detecting and locating common electronic initiators. The unintended emissions of radio receivers and microcontrollers are analyzed. These emissions are low-power radio signals that result from the device\u27s normal operation. In the first section, it is demonstrated that arbitrary signals can be injected into a radio receiver\u27s unintended emissions using a relatively weak stimulation signal. This effect is called stimulated emissions. The performance of stimulated emissions is compared to passive detection techniques. The novel technique offers a 5 to 10 dB sensitivity improvement over passive methods for detecting radio receivers. The second section develops a radar-like technique for accurately locating radio receivers. The radar utilizes the stimulated emissions technique with wideband signals. A radar-like system is designed and implemented in hardware. Its accuracy tested in a noisy, multipath-rich, indoor environment. The proposed radar can locate superheterodyne radio receivers with a root mean square position error less than 5 meters when the SNR is 15 dB or above. In the third section, an analytic model is developed for the unintended emissions of microcontrollers. It is demonstrated that these emissions consist of a periodic train of impulses. Measurements of an 8051 microcontroller validate this model. The model is used to evaluate the noise performance of several existing algorithms. Results indicate that the pitch estimation techniques have a 4 dB sensitivity improvement over epoch folding algorithms --Abstract, page iii