85 research outputs found
Spectrum Sensing of DVB-T2 Signals using a Low Computational Noise Power Estimation
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Cognitive radio is a promising technology that answers the spectrum scarcity problem arising from the proliferation of wireless networks and mobile services. In this paper, spectrum sensing of digital video broadcasting-second generation terrestrial (DVB-T2) signals in AWGN, WRAN and COST207 multipath fading environment are considered. ED is known to achieve an increased performance among low computational complexity detectors, but it is susceptible to noise uncertainty. Taking into consideration the edge pilot and scattered pilot periodicity in DVB-T2 signals, a low computational noise power estimator is proposed. Analytical forms for the detector are derived. Simulation results show that with the noise power estimator, ED significantly outperforms the pilot correlation-based detectors. Simulation also show that the proposed scheme enables ED to obtain increased detection performance in multi-path fading environments. Moreover, based on this algorithm a practical sensing scheme for cognitive radio networks is proposed.Peer reviewedFinal Accepted Versio
The information theoretic approach to signal anomaly detection for cognitive radio
Efficient utilisation and sharing of limited spectrum resources in an autonomous fashion is one of the primary goals of cognitive
radio. However, decentralised spectrum sharing can lead to interference scenarios that must be detected and characterised to help achieve the other goal of cognitive radio—reliable service for the end user. Interference events can be treated as unusual and therefore anomaly detection algorithms can be applied for their detection. Two complementary algorithms based on information theoretic measures of statistical distribution divergence and information content are proposed. The first method is applicable to signals with periodic structures and is based on the analysis of Kullback-Leibler divergence. The second utilises information content analysis to detect unusual events. Results from software and hardware implementations show that the proposed algorithms are effective, simple, and capable of processing high-speed signals in real time. Additionally, neither of the algorithms require demodulation of the signal
Amateur radio sensing technique using a combination of energy detection and waveform classification
A critical problem in spectrum sensing is to create a detection algorithm and test statistics. The existing approaches employ the energy level of each channel of interest. However, this feature cannot accurately characterize the actual application of public amateur radio. The transmitted signal is not continuous and may consist only of a carrier frequency without information. This paper proposes a novel energy detection and waveform feature classification (EDWC) algorithm to detect speech signals in public frequency bands based on energy detection and supervised machine learning. The energy level, descriptive statistics, and spectral measurements of radio channels are treated as feature vectors and classifiers to determine whether the signal is speech or noise. The algorithm is validated using actual frequency modulation (FM) broadcasting and public amateur signals. The proposed EDWC algorithm's performance is evaluated in terms of training duration, classification time, and receiver operating characteristic. The simulation and experimental outcomes show that the EDWC can distinguish and classify waveform characteristics for spectrum sensing purposes, particularly for the public amateur use case. The novel technical results can detect and classify public radio frequency signals as voice signals for speech communication or just noise, which is essential and can be applied in security aspects
Exploitation of signal information for mobile speed estimation and anomaly detection
Although the primary purpose of the signal received by amobile handset or smartphone is to
enable wireless communication, the information extracted can be reused to provide a number
of additional services. Two such services discussed in this thesis are: mobile speed estimation
and signal anomaly detection. The proposed algorithms exploit the propagation environment
specific information that is already imprinted on the received signal and therefore do not
incur any additional signalling overhead. Speed estimation is useful for providing navigation
and location based services in areas where global navigation satellite systems (GNSS) based
devices are unusable while the proposed anomaly detection algorithms can be used to locate
signal faults and aid spectrum sensing in cognitive radio systems.
The speed estimation algorithms described within this thesis require a receiver with at least
two antenna elements and a wideband radio frequency (RF) signal source. The channel transfer
function observed at the antenna elements are compared to yield an estimate of the device
speed. The basic algorithm is a one-dimensional and unidirectional two-antenna solution.
The speed of the mobile receiver is estimated from a knowledge of the fixed inter-antenna
distance and the time it takes for the trailing antenna to sense similar channel conditions previously
observed at the leading antenna. A by-product of the algorithm is an environment
specific spatial correlation function which may be combined with theoretical models of spatial
correlation to extend and improve the accuracy of the algorithm. Results obtained via
computer simulations are provided.
The anomaly detection algorithms proposed in this thesis highlight unusual signal features
while ignoring events that are nominal. When the test signal possesses a periodic frame
structure, Kullback-Leibler divergence (KLD) analysis is employed to statistically compare
successive signal frames. A method of automatically extracting the required frame period
information from the signal is also provided. When the signal under test lacks a periodic
frame structure, information content analysis of signal events can be used instead. Clean
training data is required by this algorithm to initialise the reference event probabilities. In
addition to the results obtained from extensive computer simulations, an architecture for
field-programmable gate array (FPGA) based hardware implementations of the KLD based
algorithm is provided. Results showing the performance of the algorithms against real test
signals captured over the air are also presented.
Both sets of algorithms are simple, effective and have low computational complexity – implying
that real-time implementations on platforms with limited processing power and energy
are feasible. This is an important quality since location based services are expected to be an
integral part of next generation cognitive radio handsets
Sensors for Vital Signs Monitoring
Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data
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A Cognitive Radio Compressive Sensing Framework
With the proliferation of wireless devices and services, allied with further significant predicted growth, there is an ever increasing demand for higher transmission rates. This is especially challenging given the limited availability of radio spectrum, and is further exacerbated by a rigid licensing regulatory regime. Spectrum however, is largely underutilized and this has prompted regulators to promote the concept of opportunistic spectrum access. This allows unlicensed secondary users to use bands which are licensed to primary users, but are currently unoccupied, so leading to more efficient spectrum utilization.
A potentially attractive solution to this spectrum underutilisation problem is cognitive radio (CR) technology, which enables the identification and usage of vacant bands by continuously sensing the radio environment, though CR enforces stringent timing requirements and high sampling rates. Compressive sensing (CS) has emerged as a novel sampling paradigm, which provides the theoretical basis to resolve some of these issues, especially for signals exhibiting sparsity in some domain. For CR-related signals however, existing CS architectures such as the random demodulator and compressive multiplexer have limitations in regard to the signal types used, spectrum estimation methods applied, spectral band classification and a dependence on Fourier domain based sparsity.
This thesis presents a new generic CS framework which addresses these issues by specifically embracing three original scientific contributions: i) seamless embedding of the concept of precolouring into existing CS architectures to enhance signal sparsity for CR-related digital modulation schemes; ii) integration of the multitaper spectral estimator to improve sparsity in CR narrowband modulation schemes; and iii) exploiting sparsity in an alternative, non-Fourier (Walsh-Hadamard) domain to expand the applicable CR-related modulation schemes.
Critical analysis reveals the new CS framework provides a consistently superior and robust solution for the recovery of an extensive set of currently employed CR-type signals encountered in wireless communication standards. Significantly, the generic and portable nature of the framework affords the opportunity for further extensions into other CS architectures and sparsity domains
Learning Human Behaviour Patterns by Trajectory and Activity Recognition
The world’s population is ageing, increasing the awareness of neurological and behavioural
impairments that may arise from the human ageing. These impairments can be manifested
by cognitive conditions or mobility reduction. These conditions are difficult to be
detected on time, relying only on the periodic medical appointments. Therefore, there is
a lack of routine screening which demands the development of solutions to better assist
and monitor human behaviour. The available technologies to monitor human behaviour
are limited to indoors and require the installation of sensors around the user’s homes
presenting high maintenance and installation costs. With the widespread use of smartphones,
it is possible to take advantage of their sensing information to better assist the
elderly population. This study investigates the question of what we can learn about human
pattern behaviour from this rich and pervasive mobile sensing data. A deployment
of a data collection over a period of 6 months was designed to measure three different
human routines through human trajectory analysis and activity recognition comprising
indoor and outdoor environment. A framework for modelling human behaviour was
developed using human motion features, extracted in an unsupervised and supervised
manner. The unsupervised feature extraction is able to measure mobility properties such
as step length estimation, user points of interest or even locomotion activities inferred
from an user-independent trained classifier. The supervised feature extraction was design
to be user-dependent as each user may have specific behaviours that are common to
his/her routine. The human patterns were modelled through probability density functions
and clustering approaches. Using the human learned patterns, inferences about
the current human behaviour were continuously quantified by an anomaly detection
algorithm, where distance measurements were used to detect significant changes in behaviour.
Experimental results demonstrate the effectiveness of the proposed framework
that revealed an increase potential to learn behaviour patterns and detect anomalies
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
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