Browsing by Author "Chelangat, Florence."

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An alternative approach to impulsive noise characterisation and statistical modelling for broadband powerline communication networks.
(2024) Chelangat, Florence.; Afullo, Thomas Joachim Odhiambo.
Research into the modelling of powerline communication impulsive noise – which exhibits unpredictable behaviour in its time domain characteristics of amplitude, inter-arrival time, and service time - is still in progress. As a result, in order to propose an appropriate mitigation strategy to address the interference generated by powerline communication impulsive noise, an appropriate characterization of its time domain parameters is crucial. Given the complex structure of the powerline communication network that includes a heavy wiring system, the models proposed for the various noise characteristics are stochastic in nature. In this work, extensive noise measurements were carried out over various indoor networks in the School of Engineering, University of KwaZulu-Natal, Durban, South Africa. The measurements were conducted at the following sites: the Computer Laboratory, the Machines Laboratory, the Electronic Laboratory, the Second-year Laboratory, the Post-graduate office, as well as at an adjacent apartment. This campaign was undertaken to adequately capture the behaviour of powerline communication noise, which varies randomly depending on location, time, and the devices linked to the electrical network. To begin with, the amplitude distribution of the powerline communication impulsive noise was examined. The Gaussian mixture model was used to analyse the amplitude distribution of powerline communication noise, which is essential in estimating the level of noise reaching the receiver. Gaussian mixture models are commonly employed in modelling the powerline communication impulsive noise amplitude distribution. However, the weights of the Gaussian mixture components are derived using statistical distributions, with the most common models employing the Bernoulli and Poisson distributions. These models, however, have been found to be insufficient in describing powerline communication noise. This thesis contributes to the modelling of the amplitude distribution of powerline communication impulsive noise by using unsupervised learning to determine the parameters of the Gaussian mixture. Regression analysis is also proposed to solve the issue of singularity in the likelihood function of this model as well as to determine the optimum number of Gaussian components. Further analysis of the amplitude distribution is performed using a fully Bayesian treatment referred to as the Variational Bayesian model, where the parameters of the Gaussian mixture model are assumed to be random variables, such that prior distributions over the parameters are introduced. Moreover, the optimal number of components is determined from the measurement data through the Variational Bayesian criterion. This ensures that improved accuracy due to the increased number of components in modelling the powerline communication impulsive noise amplitude distribution is eliminated thus reducing the model complexity while adequately describing the data. The variational-expectation algorithm, analogous to the expectation-maximisation algorithm employed in the Gaussian mixture model, is used to determine the model parameters. Measurements have shown that the powerline communication impulsive noise can be modelled as a superposition of several exponential distributions. Consequently, most of the research models proposed for modelling the inter-arrival and service time distribution are based on the Markov chain. There is still no defined method of evaluating the number of states, with existing models employing various curve-fitting techniques to find the optimum model for the measurement data. This work provides an alternative approach based on the queueing theory technique, where the impulsive noise occurrence in the powerline communication channel is modelled as an Erlangian queue. A straightforward method for obtaining the optimum number of exponential phases by employing the mean and the variance of the Erlang-k distribution is presented. The proposed model assumes that the impulsive noise passes through k arrival stages before entering the powerline communication network and another k service stages before leaving the powerline communication network. In all of the measurement data under consideration, impulsive noise events are observed to achieve steady-state in the inter-arrival and service time distributions. In this work, the measurements indicate that the powerline communication noise can occur as a single-impulse noise or a burst-impulse noise. The burst-impulse noise is caused by the overlap of three or more high-amplitude single-impulse noise events that occur successively in an impulse train. The amplitude of the noise, as well as the interarrival and service time distribution, vary depending on the location and time. As a result, the impulsive noise is categorised as low, medium, or highly impulsive, depending on the noise levels. The probability density function of the noise amplitude exhibits heavy tails comparable to the Gaussian mixtures. The performance of the maximum likelihood estimate and the Variational Bayesian model in finding the parameters of the Gaussian mixture are validated through measurements, where the maximum likelihood estimate yields better accuracy. However, cases of singularity are encountered in addition to an increase in performance as the number of impulsive noise components is increased. Therefore, the implementation of the Variational Bayesian approach in modelling the parameters of the Gaussian mixture enables the determination of the appropriate number of Gaussian mixture components and no singularity case is found. Although the Variational Bayesian model provides a good generalization to the measured data, the maximum likelihood technique gives better accuracy since the Variational Bayesian model provides an approximate solution, as it is based on maximising the lower bound. Both models are observed to have a high level of significance as well as a good correlation to the measured data and thus either can be used in modelling the amplitude distribution of the powerline communication noise. In modelling the inter-arrival and service time distributions, the Erlang-k distribution is observed to be more appropriate for modelling the burst-impulse noise events with a high level of significance to the measured data. The exponential distribution, which is a special case of the Erlang-k distribution, is determined to be appropriate in estimating the inter-arrival time of the single-impulse noise events, indicating high variance in the measurement data. The models proposed in this thesis can be used as simulation tools to assist the development of physical layers of powerline communication systems.
Power line communication impedance profiling and matching for broadband applications.
(2018) Chelangat, Florence.; Afullo, Thomas Joachim Odhiambo.; Mosalaosi, Modisa.
Power line communication(PLC) is a wired communication technology that has recently re- ceived a lot of attention due to its attractive prospects towards home and /or neighborhood network applications as well as smart grid technologies. It allows establishing digital com- munications without any additional wiring requirements. Effectively, one’s home and/or neighborhood wiring contributes into a smart grid to deploy various data services. It is well known that the power grid is one of the most pervasive infrastructure built to provide electricity to customers, therefore, utilizing this infrastructure for digital communications will only result in an ubiquitous telecommunications network. It is common practice to use wires to establish a physical connection in many telecommunications channels, but most electronic devices already have a pair of wires connected to the power lines. Therefore, these wires can be used to simultaneously establish digital communications. Thus, power line communications can be used as an alternative solution to more established technologies such as wireless, coaxial and optical communications. As a promising technology, PLC has attracted a lot of research and has become an active area of research which continues to evolve over time. Notwithstanding its advantages, PLC has issues, namely, severe noise at low frequencies and varying characteristic impedance. This is primarily because the power line channel was not originally designed to be used for communications, thus, it remains a harsh channel. Other challenges arise from the fact that there are different wiring practices around the world, unpredictable loading characteristics as well as differential- and common-mode characteristic impedance. As a result, there is a considerable amount of noise signal attenuation during data transmission. Loss of signal can be addressed by increasing the power at the transmitter, noise reduction and/or reducing channel attenuation to improve the signal-to-noise ratio. However, PLC modems are subject to legislation that impose a limit with regards to the signal levels in the lines. Power lines are good radiators at high frequencies which makes them behave like large antennas with the ability to intercept other radiations in the same frequency range. The radiated signal is proportional to the currents in the line, thus, increasing line currents will not solve the problem but would rather lead to violation of electromagnetic compatibility (EMC) regulations. In this work, an alternative solution is provided which seeks to address the issue of signal attenuation caused by the changing input impedance of a typical power line channel. The deleterious effects of noise are not considered since this work focuses on broadband PLC in the 1–30 MHz frequency range. The objective of this work was to design and build an impedance adaptive coupler to mitigate effects of channel attenuation caused by varying impedance. In this way, the propagating signal will “see” a uniform impedance and as a result the data output will be improved. The work was facilitated by measuring several impedance profiles of PLC channels in the band of interest. Typically, the network topology of PLC networks is not known and the building architectural blueprints are not always readily available. To overcome this issue,this work was performed on power line test-beds designed to mimic varied typical PLC network topologies. Moreover, there is an additional benefit in that it is possible to relate the output impedance profile to the network topology. The channel input impedance characteristics were determined in a deterministic manner by considering a power line network as a cascade of parallel resonant circuits and applying transmission line theory to develop the model. The model was validated by measurements with good agreement over the frequency range was considered. Several measurements were then used to determine the minimum, average and maximum input impedance that a signal will experience as it traverses the channel. It was found that, regardless of the network size (in terms of number of branches), the average input impedance is 354 ± 1.1 % Ω in the 1-30 MHz frequency band. Due to the unpredictable nature of the input impedance of the power line network, an impedance adaptive bidirectional coupler for broadband power line communications was designed. The impedance matching is achieved by using typical L-section matching networks in the 1–30 MHz band. The matching section of the coupler has the characteristics of a lowpass filter while the coupling section is a highpass filter, effectively forming a bandpass network. The simulated transfer characteristics of the designed coupler performs very well for impedances starting around 150 Ω and the performance improves a great deal as the impedance increases. The coupler can still be improved to accommodate much lower input impedances (as low as 50 Ω). However, based on the measured results of input impedance, it was observed that the power line channel impedance is statistically higher than 200 Ω most of the time which makes the presented design acceptable.