A NOISE ESTIMATION SCHEME FOR BLIND SPECTRUM SENSING USING EMD

The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. One solution is to use underutilized spectrum. Cognitive Radio (CR) technologies identify transmission opportunities in unused channels and avoid interfering with primary users. The key enabling technology is the Spectrum Sensing (SS). Different SS techniques exist, but techniques that do not require knowledge of the signals (non-coherent) are preferred. Noise estimation plays an essential role in enhancing the performance of non-coherent spectrum sensors such as energy detectors. In this thesis, we present an energy detector based on the behavior of Empirical Mode Decomposition (EMD) towards vacant channels (noise-dominant). The energy trend from the EMD processed signal is used to determine the occupancy of a given band of interest. The performance of the proposed EMD-based detector is evaluated for different noise levels and sample sizes. Further, a comparison is carried out with conventional spectrum sensing techniques to validate the efficacy of the proposed detector and the results revealed that it outperforms the other sensing methods

Spectrum sensing algorithms and software-defined radio implementation for cognitive radio system

The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities

Distribution Based Spectrum Sensing in Cognitive Radio

Blind spectrum sensing in cognitive radio is being addressed in this thesis. Particular emphasis is put on performance in the low signal to noise range. It is shown how methods relying on traditional sample based estimation methods, such as the energy detector and autocorrelation based detectors, suffer at low SNRs. This problem is attempted to be solved by investigating how higher order statistics and information theoretic distance measures can be applied to do spectrum sensing. Results from a thorough literature survey indicate that the information theoretic distance gls{kl} divergence is promising when trying to devise a novel cognitive radio spectrum sensing scheme. Two novel detection algorithms based on Kullback-Leibler divergence estimation are proposed. However, unfortunately only one of them has a fully proven theoretical foundation. The other has a partial theoretical framework, supported by empirical results. Detection performance of the two proposed detectors in comparison with two reference detectors is assessed. The two reference detectors are the energy detector, and an autocorrelation based detector. Through simulations, it is shown that the proposed KL divergence based algorithms perform worse than the energy detector for all the considered scenarios, while one of them performs better than the autocorrelation based detector for certain signals. The reason why the detectors perform worse than the energy detector, despite the good properties of the estimators at low signal to noise ratios, is that the KL divergence between signal and noise is small. The low divergence stems from the fact that both signal and noise have very similar probability density distributions. Detection performance is also assessed by applying the detectors to raw data of a downconverted UMTS signal. It is shown that the noise distribution deviates from the standard assumption (circularly symmetric complex white Gaussian). Due to this deviation, the autocorrelation based reference detector and the two proposed Kullback-Leibler divergence based detectors are challenged. These detectors rely heavily on the aforementioned assumption, and fail to function properly when applied to signals with deviating characteristics

Distribution Based Spectrum Sensing in Cognitive Radio

Blind spectrum sensing in cognitive radio is being addressed in this thesis. Particular emphasis is put on performance in the low signal to noise range. It is shown how methods relying on traditional sample based estimation methods, such as the energy detector and autocorrelation based detectors, suffer at low SNRs. This problem is attempted to be solved by investigating how higher order statistics and information theoretic distance measures can be applied to do spectrum sensing. Results from a thorough literature survey indicate that the information theoretic distance gls{kl} divergence is promising when trying to devise a novel cognitive radio spectrum sensing scheme. Two novel detection algorithms based on Kullback-Leibler divergence estimation are proposed. However, unfortunately only one of them has a fully proven theoretical foundation. The other has a partial theoretical framework, supported by empirical results. Detection performance of the two proposed detectors in comparison with two reference detectors is assessed. The two reference detectors are the energy detector, and an autocorrelation based detector. Through simulations, it is shown that the proposed KL divergence based algorithms perform worse than the energy detector for all the considered scenarios, while one of them performs better than the autocorrelation based detector for certain signals. The reason why the detectors perform worse than the energy detector, despite the good properties of the estimators at low signal to noise ratios, is that the KL divergence between signal and noise is small. The low divergence stems from the fact that both signal and noise have very similar probability density distributions. Detection performance is also assessed by applying the detectors to raw data of a downconverted UMTS signal. It is shown that the noise distribution deviates from the standard assumption (circularly symmetric complex white Gaussian). Due to this deviation, the autocorrelation based reference detector and the two proposed Kullback-Leibler divergence based detectors are challenged. These detectors rely heavily on the aforementioned assumption, and fail to function properly when applied to signals with deviating characteristics