1,492 research outputs found
Eigenvalue-based Cyclostationary Spectrum Sensing Using Multiple Antennas
In this paper, we propose a signal-selective spectrum sensing method for
cognitive radio networks and specifically targeted for receivers with
multiple-antenna capability. This method is used for detecting the presence or
absence of primary users based on the eigenvalues of the cyclic covariance
matrix of received signals. In particular, the cyclic correlation significance
test is used to detect a specific signal-of-interest by exploiting knowledge of
its cyclic frequencies. The analytical threshold for achieving constant false
alarm rate using this detection method is presented, verified through
simulations, and shown to be independent of both the number of samples used and
the noise variance, effectively eliminating the dependence on accurate noise
estimation. The proposed method is also shown, through numerical simulations,
to outperform existing multiple-antenna cyclostationary-based spectrum sensing
algorithms under a quasi-static Rayleigh fading channel, in both spatially
correlated and uncorrelated noise environments. The algorithm also has
significantly lower computational complexity than these other approaches.Comment: 6 pages, 6 figures, accepted to IEEE GLOBECOM 201
An Innovative Signal Detection Algorithm in Facilitating the Cognitive Radio Functionality for Wireless Regional Area Network Using Singular Value Decomposition
This thesis introduces an innovative signal detector algorithm in facilitating the
cognitive radio functionality for the new IEEE 802.22 Wireless Regional Area
Networks (WRAN) standard. It is a signal detector based on a Singular Value
Decomposition (SVD) technique that utilizes the eigenvalue of a received signal. The
research started with a review of the current spectrum sensing methods which the
research classifies as the specific, semiblind or blind signal detector. A blind signal detector, which is known as eigenvalue based detection, was found to be the most
desired detector for its detection capabilities, time of execution, and zero a-priori knowledge. The detection algorithm was developed analytically by applying the Signal Detection Theory (SDT) and the Random Matrix Theory (RMT). It was then simulated
using Matlab® to test its performance and compared with similar eigenvalue based
signal detector. There are several techniques in finding eigenvalues. However, this
research considered two techniques known as eigenvalue decomposition (EVD) and
SVD. The research tested the algorithm with a randomly generated signal, simulated
Digital Video Broadcasting-Terrestrial (DVB-T) standard and real captured digital
television signals based on the Advanced Television Systems Committee (ATSC)
standard. The SVD based signal detector was found to be more efficient in detecting
signals without knowing the properties of the transmitted signal. The algorithm is
suitable for the blind spectrum sensing where the properties of the signal to be detected
are unknown. This is also the advantage of the algorithm since any signal would
interfere and subsequently affect the quality of service (QoS) of the IEEE 802.22
connection. Furthermore, the algorithm performed better in the low signal-to-noise
ratio (SNR) environment. In order to use the algorithm effectively, users need to
balance between detection accuracy and execution time. It was found that a higher
number of samples would lead to more accurate detection, but will take longer time.
In contrary, fewer numbers of samples used would result in less accuracy, but faster
execution time. The contributions of this thesis are expected to assist the IEEE
802.22 Standard Working Group, regulatory bodies, network operators and end-users
in bringing broadband access to the rural areas
Evaluation of blind sensing techniques in multiple wireless microphones environments
This work focuses on the evaluation of blind sensing techniques for the detection of multiple wireless microphones in the UHF band, by means of simulation. The metrics used for the comparisons include probability of detection, probability of false alarm and minimum SNR detected for a given observation time. As an example, simulation results showed that blind detection algorithms can sense multiple wireless microphone signals with SNR = -19 dB, in a Rayleigh channel environment, considering 100 ms sensing time, 90 % probability of detection and 10 % probability of false alarm. In these conditions, blind detection techniques suffer maximum SNR degradation of 3.5 dB, as compared with single wireless microphone scenarios.info:eu-repo/semantics/publishedVersio
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
A Cooperative Spectrum Sensing Network with Signal Classification Capabilities
This report describes the design and implementation of the spectrum sensing and signal classification sub-systems of a cooperative network. A sensor blindly receives and calculates the cyclic statistics of a signal decides whether or not the signal represents information or noise. If the signal\u27s statistics indicate the presence of data, the system attempts to classify its modulation scheme. Finally, the decisions of several independent sensors are combined to provide a reliable estimate of the contents of the spectrum of interest. Independently, sensors correctly classify a signal about 60-70% of the time in a low SNR environment. The data fusion module improves this number significantly - especially as the number of sensors increases
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