142 research outputs found
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Cooperative Precoding with Limited Feedback for MIMO Interference Channels
Multi-antenna precoding effectively mitigates the interference in wireless
networks. However, the resultant performance gains can be significantly
compromised in practice if the precoder design fails to account for the
inaccuracy in the channel state information (CSI) feedback. This paper
addresses this issue by considering finite-rate CSI feedback from receivers to
their interfering transmitters in the two-user multiple-input-multiple-output
(MIMO) interference channel, called cooperative feedback, and proposing a
systematic method for designing transceivers comprising linear precoders and
equalizers. Specifically, each precoder/equalizer is decomposed into inner and
outer components for nulling the cross-link interference and achieving array
gain, respectively. The inner precoders/equalizers are further optimized to
suppress the residual interference resulting from finite-rate cooperative
feedback. Further- more, the residual interference is regulated by additional
scalar cooperative feedback signals that are designed to control transmission
power using different criteria including fixed interference margin and maximum
sum throughput. Finally, the required number of cooperative precoder feedback
bits is derived for limiting the throughput loss due to precoder quantization.Comment: 23 pages; 5 figures; this work was presented in part at Asilomar 2011
and will appear in IEEE Trans. on Wireless Com
Optimization and learning algorithms for orthogonal frequency division multiplexing-based dynamic spectrum access.
Ph.DDOCTOR OF PHILOSOPH
A Mobile Wireless Channel State Recognition Algorihm: Introduction, Definition, and Verification - Sensing for Cognitive Environmental Awareness
This research includes mobile wireless systems limited by time and frequency dispersive channels. A blind mobile wireless channel (MWC) state recognition (CSR) algorithm that detects hidden coherent nonselective and noncoherent selective processes is verified. Because the algorithm is blind, it releases capacity based on current channel state that traditionally is fixed and reserved for channel gain estimation and distortion mitigation. The CSR algorithm enables cognitive communication system control including signal processing, resource allocation/deallocation, or distortion mitigation selections based on channel coherence states. MWC coherent and noncoherent states, ergodicity, stationarity, uncorrelated scattering, and Markov processes are assumed for each time block. Furthermore, a hidden Markov model (HMM) is utilized to represent the statistical relationships between hidden dispersive processes and observed receive waveform processes. First-order and second-order statistical extracted features support state hard decisions which are combined in order to increase the accuracy of channel state estimates. This research effort has architected, designed, and verified a blind statistical feature recognition algorithm capable of detecting coherent nonselective, single time selective, single frequency selective, or dual selective noncoherent states. A MWC coherence state model (CSM) was designed to represent these hidden dispersive processes. Extracted statistical features are input into a parallel set of trained HMMs that compute state sequence conditional likelihoods. Hard state decisions are combined to produce a single most likely channel state estimate for each time block. To verify the CSR algorithm performance, combinations of hidden state sequences are applied to the CSR algorithm and verified against input hidden state sequences. State sequence recognition accuracy sensitivity was found to be above 99% while specificity was determined to be above 98% averaged across all features, states, and sequences. While these results establish the feasibility of a MWC blind CSR algorithm, optimal configuration requires future research to further improve performance including: 1) characterizing the range of input signal configurations, 2) waveform feature block size reduction, 3) HMM parameter tracking, 4) HMM computational complexity and latency reduction, 5) feature soft decision combining, 6) recursive implementation, 7) interfacing with state based mobile wireless communication control processes, and 8) extension to wired or wireless waveform recognition
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
- …