26 research outputs found
Results analysis and validation - D5.3
Deliverable D5.3 del projecte OneFITPostprint (author’s final draft
Comparison among Cognitive Radio Architectures for Spectrum Sensing
Recently, the growing success of new wireless applications and services has led to overcrowded licensed bands, inducing the governmental regulatory agencies to consider more flexible strategies to improve the utilization of the radio spectrum. To this end, cognitive radio represents a promising technology since it allows to exploit the unused radio resources. In this context, the spectrum sensing task is one of the most challenging issues faced by a cognitive radio. It consists of an analysis of the radio environment to detect unused resources which can be exploited by cognitive radios. In this paper, three different cognitive radio architectures, namely, stand-alone single antenna, cooperative and multiple antennas, are proposed for spectrum sensing purposes. These architectures implement a relatively fast and reliable signal processing algorithm, based on a feature detection technique and support vector machines, for identifying the transmissions in a given environment. Such architectures are compared in terms of detection and classification performances for two transmission standards, IEEE 802.11a and IEEE 802.16e. A set of numerical simulations have been carried out in a challenging scenario, and the advantages and disadvantages of the proposed architectures are discussed
Spectrum Optimisation in Wireless Communication Systems: Technology Evaluation, System Design and Practical Implementation
Two key technology enablers for next generation networks are examined in this thesis, namely Cognitive Radio (CR) and Spectrally Efficient Frequency Division Multiplexing (SEFDM). The first part proposes the use of traffic prediction in CR systems to improve the Quality of Service (QoS) for CR users. A framework is presented which allows CR users to capture a frequency slot in an idle licensed channel occupied by primary users. This is achieved by using CR to sense and select target spectrum bands combined with traffic prediction to determine the optimum channel-sensing order. The latter part of this thesis considers the design, practical implementation and performance evaluation of SEFDM. The key challenge that arises in SEFDM is the self-created interference which complicates the design of receiver architectures. Previous work has focused on the development of sophisticated detection algorithms, however, these suffer from an impractical computational complexity. Consequently, the aim of this work is two-fold; first, to reduce the complexity of existing algorithms to make them better-suited for application in the real world; second, to develop hardware prototypes to assess the feasibility of employing SEFDM in practical systems. The impact of oversampling and fixed-point effects on the performance of SEFDM is initially determined, followed by the design and implementation of linear detection techniques using Field Programmable Gate Arrays (FPGAs). The performance of these FPGA based linear receivers is evaluated in terms of throughput, resource utilisation and Bit Error Rate (BER). Finally, variants of the Sphere Decoding (SD) algorithm are investigated to ameliorate the error performance of SEFDM systems with targeted reduction in complexity. The Fixed SD (FSD) algorithm is implemented on a Digital Signal Processor (DSP) to measure its computational complexity. Modified sorting and decomposition strategies are then applied to this FSD algorithm offering trade-offs between execution speed and BER
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
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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
Results analysis and validation - D5.3
This deliverable describes the validation processes followed to assess the performance of the algorithms and protocols for the operator governed opportunistic networking as defined in the OneFIT Project. Therefore, this document includes the description of the set-up of the different validation platforms, the design of the test plans for each one of them, and the analysis of the results obtained from the tests. A per-scenario approach rather than a per-platform approach has been followed, so an additional analysis has been performed, gathering the results related to each scenario, in order to validate the premises stated to each one of them. The OneFIT concept has been therefore validated for all foreseen business scenarios
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
Spectrum sensing for cognitive radio and radar systems
The use of the radio frequency spectrum is increasing at a rapid rate. Reliable and efficient operation in a crowded radio spectrum requires innovative solutions and techniques. Future wireless communication and radar systems should be aware of their surrounding radio environment in order to have the ability to adapt their operation to the effective situation. Spectrum sensing techniques such as detection, waveform recognition, and specific emitter identification are key sources of information for characterizing the surrounding radio environment and extracting valuable information, and consequently adjusting transceiver parameters for facilitating flexible, efficient, and reliable operation.
In this thesis, spectrum sensing algorithms for cognitive radios and radar intercept receivers are proposed. Single-user and collaborative cyclostationarity-based detection algorithms are proposed: Multicycle detectors and robust nonparametric spatial sign cyclic correlation based fixed sample size and sequential detectors are proposed. Asymptotic distributions of the test statistics under the null hypothesis are established. A censoring scheme in which only informative test statistics are transmitted to the fusion center is proposed for collaborative detection. The proposed detectors and methods have the following benefits: employing cyclostationarity enables distinction among different systems, collaboration mitigates the effects of shadowing and multipath fading, using multiple strong cyclic frequencies improves the performance, robust detection provides reliable performance in heavy-tailed non-Gaussian noise, sequential detection reduces the average detection time, and censoring improves energy efficiency.
In addition, a radar waveform recognition system for classifying common pulse compression waveforms is developed. The proposed supervised classification system classifies an intercepted radar pulse to one of eight different classes based on the pulse compression waveform: linear frequency modulation, Costas frequency codes, binary codes, as well as Frank, P1, P2, P3, and P4 polyphase codes.
A robust M-estimation based method for radar emitter identification is proposed as well. A common modulation profile from a group of intercepted pulses is estimated and used for identifying the radar emitter. The M-estimation based approach provides robustness against preprocessing errors and deviations from the assumed noise model
Performance analysis of FBMC over OFDM in Cognitive Radio Network
Cognitive Radio (CR) system is an adaptive, reconfigurable communication system that can intuitively adjust its parameters to meet users or network demands. The major objective of CR is to provide a platform for the Secondary User (SU) to fully utilize the available spectrum resource by sensing the existence of spectrum holes without causing interference to the Primary User (PU). However, PU detection has been one of the main challenges in CR technology. In comparison to traditional wireless communication systems, due to the Cross-Channel Interference (CCI) from the adjacent channels used by SU to PU, CR system now poses new challenges to Resource Allocation (RA) problems. Past efforts have been focussed on Orthogonal Frequency Division Multiplexing (OFDM) based CR systems. However, OFDM technique show various limitations in CR application due to its enormous spectrum leakage. Filter Bank based Multicarrier (FBMC) has been proposed as a promising Multicarrier Modulation (MCM) candidate that has numerous advantages over OFDM. In this dissertation, a critical analysis of the performance of FBMC over OFDM was studied, and CR system was used as the testing platform. Firstly, the problem of spectrum sensing of OFDM based CR systems in contrast to FBMC based were surveyed from literature point of view, then the performance of the two schemes was analysed and compared from the spectral efficiency point of view. A resource allocation algorithm was proposed where much attention was focused on interference and power constraint. The proposed algorithms have been verified using MATLAB simulations, however, numerical results show that FBMC can attain higher spectrum efficiency and attractive benefit in terms of spectrum sensing as opposed to OFDM. The contributions of this dissertation have heightened the interest in more research and findings on how FBMC can be improved for future application CR systems
Opportunistic Access in Frequency Hopping Cognitive Radio Networks
Researchers in the area of cognitive radio often investigate the utility of dynamic spectrum access as a means to make more efficient use of the radio frequency spectrum. Many studies have been conducted to find ways in which a secondary user can occupy spectrum licensed to a primary user in a manner which does not disrupt the primary user\u27s performance. This research investigates the use of opportunistic access in a frequency hopping radio to mitigate the interference caused by other transmitters in a contentious environment such as the unlicensed 2.4 GHz region. Additionally, this work demonstrates how dynamic spectrum access techniques can be used not only to prevent interfering with other users but also improve the robustness of a communication system