6 research outputs found
Classification and modeling of power line noise using machine learning techniques
A thesis submitted in ful lment of the requirements
for the degree of Doctor of Philosophy
in the
School of Electrical and Information Engineering
Faculty of Engineering and Built Environment
June 2017The realization of robust, reliable and e cient data transmission have been the theme of
recent research, most importantly in real channel such as the noisy, fading prone power
line communication (PLC) channel. The focus is to exploit old techniques or create new
techniques capable of improving the transmission reliability and also increasing the transmission
capacity of the real communication channels. Multi-carrier modulation scheme such
as Orthogonal Frequency Division Multiplexing (OFDM) utilizing conventional single-carrier
modulation is developed to facilitate a robust data transmission, increasing transmission capacity
(e cient bandwidth usage) and further reducing design complexity in PLC systems.
On the contrary, the reliability of data transmission is subjected to several inhibiting factors
as a result of the varying nature of the PLC channel. These inhibiting factors include noise,
perturbation and disturbances. Contrary to the Additive White Gaussian noise (AWGN)
model often assumed in several communication systems, this noise model fails to capture
the attributes of noise encountered on the PLC channel. This is because periodic noise or
random noise pulses injected by power electronic appliances on the network is a deviation
from the AWGN. The nature of the noise is categorized as non-white non-Gaussian and
unstable due to its impulsive attributes, thus, it is labeled as Non-additive White Gaussian
Noise (NAWGN). These noise and disturbances results into long burst errors that corrupts
signals being transmitted, thus, the PLC is labeled as a horrible or burst error channel.
The e cient and optimal performance of a conventional linear receiver in the white Gaussian
noise environment can therefore be made to drastically degrade in this NAWGN environment.
Therefore, transmission reliability in such environment can be greatly enhanced if we
know and exploit the knowledge of the channel's statistical attributes, thus, the need for
developing statistical channel model based on empirical data. In this thesis, attention is
focused on developing a recon gurable software de ned un-coded single-carrier and multicarrier
PLC transceiver as a tool for realizing an optimized channel model for the narrowband
PLC (NB-PLC) channel.
First, a novel recon gurable software de ned un-coded single-carrier and multi-carrier PLC
transceiver is developed for real-time NB-PLC transmission. The transceivers can be adapted
to implement di erent waveforms for several real-time scenarios and performance evaluation.
Due to the varying noise parameters obtained from country to country as a result of
the dependence of noise impairment on mains voltages, topology of power line, place and
time, the developed transceivers is capable of facilitating constant measurement campaigns
to capture these varying noise parameters before statistical and mathematically inclined
channel models are derived.
Furthermore, the single-carrier (Binary Phase Shift Keying (BPSK), Di erential BPSK
(DBPSK), Quadrature Phase Shift Keying (QPSK) and Di erential QPSK (DQPSK)) PLC
transceiver system developed is used to facilitate a First-Order semi-hidden Fritchman
Markov modeling (SHFMM) of the NB-PLC channel utilizing the e cient iterative Baum-
Welch algorithm (BWA) for parameter estimation. The performance of each modulation
scheme is evaluated in a mildly and heavily disturbed scenarios for both residential and
laboratory site considered. The First-Order estimated error statistics of the realized First-
Order SHFMM have been analytically validated in terms of performance metrics such as:
log-likelihood ratio (LLR), error-free run distribution (EFRD), error probabilities, mean
square error (MSE) and Chi-square ( 2) test. The reliability of the model results is also
con rmed by an excellent match between the empirically obtained error sequence and the
SHFMM regenerated error sequence as shown by the error-free run distribution plot.
This thesis also reports a novel development of a low cost, low complexity Frequency-shift
keying (FSK) - On-o keying (OOK) in-house hybrid PLC and VLC system. The functionality
of this hybrid PLC-VLC transceiver system was ascertained at both residential and
laboratory site at three di erent times of the day: morning, afternoon and evening. A First
and Second-Order SHFMM of the hybrid system is realized. The error statistics of the realized
First and Second-Order SHFMMs have been analytically validated in terms of LLR,
EFRD, error probabilities, MSE and Chi-square ( 2). The Second-Order SHFMMs have
also been analytically validated to be superior to the First-Order SHFMMs although at the
expense of added computational complexity. The reliability of both First and Second-Order
SHFMM results is con rmed by an excellent match between the empirical error sequences
and SHFMM re-generated error sequences as shown by the EFRD plot.
In addition, the multi-carrier (QPSK-OFDM, Di erential QPSK (DQPSK)-OFDM) and
Di erential 8-PSK (D8PSK)-OFDM) PLC transceiver system developed is used to facilitate
a First and Second-Order modeling of the NB-PLC system using the SHFMM and BWA
for parameter estimation. The performance of each OFDM modulation scheme in evaluated
and compared taking into consideration the mildly and heavily disturbed noise scenarios
for the two measurement sites considered. The estimated error statistics of the realized
SHFMMs have been analytically validated in terms of LLR, EFRD, error probabilities, MSE
and Chi-square ( 2) test. The estimated Second-Order SHFMMs have been analytically
validated to be outperform the First-Order SHFMMs although with added computational
complexity. The reliability of the models is con rmed by an excellent match between the
empirical data and SHFMM generated data as shown by the EFRD plot.
The statistical models obtained using Baum-Welch to adjust the parameters of the adopted
SHFMM are often locally maximized. To solve this problem, a novel Metropolis-Hastings
algorithm, a Bayesian inference approach based on Markov Chain Monte Carlo (MCMC)
is developed to optimize the parameters of the adopted SHFMM. The algorithm is used to
optimize the model results obtained from the single-carrier and multi-carrier PLC systems
as well as that of the hybrid PLC-VLC system. Consequently, as deduced from the results,
the models obtained utilizing the novel Metropolis-Hastings algorithm are more precise, near
optimal model with parameter sets that are closer to the global maxima.
Generally, the model results obtained in this thesis are relevant in enhancing transmission
reliability on the PLC channel through the use of the models to improve the adopted modulation
schemes, create adaptive modulation techniques, develop and evaluate forward error
correction (FEC) codes such as a concatenation of Reed-Solomon and Permutation codes and
other robust codes suitable for exploiting and mitigating noise impairments encountered on
the low voltage NB-PLC channel. Furthermore, the recon gurable software de ned NB-PLC
transceiver test-bed developed can be utilized for future measurement campaign as well as
adapted for multiple-input and multiple-output (MIMO) PLC applications.MT201
Classification and modeling of power line noise using machine learning techniques
A thesis submitted in ful lment of the requirements
for the degree of Doctor of Philosophy
in the
School of Electrical and Information Engineering
Faculty of Engineering and Built Environment
June 2017The realization of robust, reliable and e cient data transmission have been the theme of
recent research, most importantly in real channel such as the noisy, fading prone power
line communication (PLC) channel. The focus is to exploit old techniques or create new
techniques capable of improving the transmission reliability and also increasing the transmission
capacity of the real communication channels. Multi-carrier modulation scheme such
as Orthogonal Frequency Division Multiplexing (OFDM) utilizing conventional single-carrier
modulation is developed to facilitate a robust data transmission, increasing transmission capacity
(e cient bandwidth usage) and further reducing design complexity in PLC systems.
On the contrary, the reliability of data transmission is subjected to several inhibiting factors
as a result of the varying nature of the PLC channel. These inhibiting factors include noise,
perturbation and disturbances. Contrary to the Additive White Gaussian noise (AWGN)
model often assumed in several communication systems, this noise model fails to capture
the attributes of noise encountered on the PLC channel. This is because periodic noise or
random noise pulses injected by power electronic appliances on the network is a deviation
from the AWGN. The nature of the noise is categorized as non-white non-Gaussian and
unstable due to its impulsive attributes, thus, it is labeled as Non-additive White Gaussian
Noise (NAWGN). These noise and disturbances results into long burst errors that corrupts
signals being transmitted, thus, the PLC is labeled as a horrible or burst error channel.
The e cient and optimal performance of a conventional linear receiver in the white Gaussian
noise environment can therefore be made to drastically degrade in this NAWGN environment.
Therefore, transmission reliability in such environment can be greatly enhanced if we
know and exploit the knowledge of the channel's statistical attributes, thus, the need for
developing statistical channel model based on empirical data. In this thesis, attention is
focused on developing a recon gurable software de ned un-coded single-carrier and multicarrier
PLC transceiver as a tool for realizing an optimized channel model for the narrowband
PLC (NB-PLC) channel.
First, a novel recon gurable software de ned un-coded single-carrier and multi-carrier PLC
transceiver is developed for real-time NB-PLC transmission. The transceivers can be adapted
to implement di erent waveforms for several real-time scenarios and performance evaluation.
Due to the varying noise parameters obtained from country to country as a result of
the dependence of noise impairment on mains voltages, topology of power line, place and
time, the developed transceivers is capable of facilitating constant measurement campaigns
to capture these varying noise parameters before statistical and mathematically inclined
channel models are derived.
Furthermore, the single-carrier (Binary Phase Shift Keying (BPSK), Di erential BPSK
(DBPSK), Quadrature Phase Shift Keying (QPSK) and Di erential QPSK (DQPSK)) PLC
transceiver system developed is used to facilitate a First-Order semi-hidden Fritchman
Markov modeling (SHFMM) of the NB-PLC channel utilizing the e cient iterative Baum-
Welch algorithm (BWA) for parameter estimation. The performance of each modulation
scheme is evaluated in a mildly and heavily disturbed scenarios for both residential and
laboratory site considered. The First-Order estimated error statistics of the realized First-
Order SHFMM have been analytically validated in terms of performance metrics such as:
log-likelihood ratio (LLR), error-free run distribution (EFRD), error probabilities, mean
square error (MSE) and Chi-square ( 2) test. The reliability of the model results is also
con rmed by an excellent match between the empirically obtained error sequence and the
SHFMM regenerated error sequence as shown by the error-free run distribution plot.
This thesis also reports a novel development of a low cost, low complexity Frequency-shift
keying (FSK) - On-o keying (OOK) in-house hybrid PLC and VLC system. The functionality
of this hybrid PLC-VLC transceiver system was ascertained at both residential and
laboratory site at three di erent times of the day: morning, afternoon and evening. A First
and Second-Order SHFMM of the hybrid system is realized. The error statistics of the realized
First and Second-Order SHFMMs have been analytically validated in terms of LLR,
EFRD, error probabilities, MSE and Chi-square ( 2). The Second-Order SHFMMs have
also been analytically validated to be superior to the First-Order SHFMMs although at the
expense of added computational complexity. The reliability of both First and Second-Order
SHFMM results is con rmed by an excellent match between the empirical error sequences
and SHFMM re-generated error sequences as shown by the EFRD plot.
In addition, the multi-carrier (QPSK-OFDM, Di erential QPSK (DQPSK)-OFDM) and
Di erential 8-PSK (D8PSK)-OFDM) PLC transceiver system developed is used to facilitate
a First and Second-Order modeling of the NB-PLC system using the SHFMM and BWA
for parameter estimation. The performance of each OFDM modulation scheme in evaluated
and compared taking into consideration the mildly and heavily disturbed noise scenarios
for the two measurement sites considered. The estimated error statistics of the realized
SHFMMs have been analytically validated in terms of LLR, EFRD, error probabilities, MSE
and Chi-square ( 2) test. The estimated Second-Order SHFMMs have been analytically
validated to be outperform the First-Order SHFMMs although with added computational
complexity. The reliability of the models is con rmed by an excellent match between the
empirical data and SHFMM generated data as shown by the EFRD plot.
The statistical models obtained using Baum-Welch to adjust the parameters of the adopted
SHFMM are often locally maximized. To solve this problem, a novel Metropolis-Hastings
algorithm, a Bayesian inference approach based on Markov Chain Monte Carlo (MCMC)
is developed to optimize the parameters of the adopted SHFMM. The algorithm is used to
optimize the model results obtained from the single-carrier and multi-carrier PLC systems
as well as that of the hybrid PLC-VLC system. Consequently, as deduced from the results,
the models obtained utilizing the novel Metropolis-Hastings algorithm are more precise, near
optimal model with parameter sets that are closer to the global maxima.
Generally, the model results obtained in this thesis are relevant in enhancing transmission
reliability on the PLC channel through the use of the models to improve the adopted modulation
schemes, create adaptive modulation techniques, develop and evaluate forward error
correction (FEC) codes such as a concatenation of Reed-Solomon and Permutation codes and
other robust codes suitable for exploiting and mitigating noise impairments encountered on
the low voltage NB-PLC channel. Furthermore, the recon gurable software de ned NB-PLC
transceiver test-bed developed can be utilized for future measurement campaign as well as
adapted for multiple-input and multiple-output (MIMO) PLC applications.MT201
Noise modeling for standard CENELEC A-band power line communication channel
Power line communications (PLC) usage of low-voltage electrical power supply network as a medium of communication provides an alternative for the telecommunication access and in-house communication. Historically, power lines were majorly used for controlling appliances, however, with recent technology advancements power lines are now able to compete favorably and successfully with other relatively stable home automation and networking technologies like fixed line and wireless.
Regardless of the advantages PLC has to offer, like every other communication technology, it has its own technical challenges it must overcome to be fully deployed and maximize its full potential. Such challenges includes noise, which can originate from appliances connected across the network or can be coupled unto the network. Harmful interference to other wireless spectrum users such as broadcast stations, and signal attenuation are other challenges faced by usage of the power line as a communication medium. PLC suffers the risk of not living up to its full development as a reliable means of communication if proper understanding of the channel potential and characteristic is not known. Therefore, understanding of the channel potential and characteristics can be obtained through measurement and modeling of the PLC channel. This model and measurements of the channel characteristics can then be utilized in designing a good PLC system which is able to withstand and mitigate the effect of the different kind of noise and disturbance present on the PLC network.
This research therefore aims at formulizing and modeling the error pattern/behavior of noise and disturbances of an in-house CENELEC A-band based on experimental measurements. This is achieved by carrying out a real time experimental measurement of noise over a complete day to show the noise behavior. Error sequences are then generated from the measurement for the different classes of noise present on the CENELEC A-band and the use of Fritchman model, a Markovian chain model, is then employed to model the CENELEC A-band channel. This involves the use of Baum-Welch algorithm (an iterative algorithm) to estimate the model parameters of the three-state Markovian Fritchman model assumed. This precise channel model can then be used to design a good PLC system and facilitate the design of efficient coding and/or modulation schemes to enhance reliable communication on the PLC network. Therefore, answering the question of “how to formulize and model the error pattern/behavior of noise and disturbances of an in-house CENELEC A-band based on experimental measurements”
A Block Diagonal Markov model for indoor software-defined power line communication
Abstract: A Semi-Hidden Markov Model (SHMM) for bursty error channels is defined by a state transition probability matrix A, a prior probability vector Π, and the state dependent output symbol error probability matrix B. Several processes are utilized for estimating A, Πand B from a given empirically obtained or simulated error sequence. However, despite placing some restrictions on the underlying Markov model structure, we still have a computationally intensive estimation procedure, especially given a large error sequence containing long burst of identical symbols. Thus, in this paper, we utilize under some moderate assumptions, a Markov model with random state transition matrix A equivalent to a unique Block Diagonal Markov model with state transition matrix Λ to model an indoor software- defined power line communication system. A computationally efficient modified Baum-Welch algorithm for estimation of Λ given an experimentally obtained error sequence from the indoor PLC channel is utilized. Resulting Equivalent Block Diagonal Markov models assist designers to accelerate and facilitate the procedure of novel PLC systems design and evaluation