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1
Balakrishnan, Gautam. "Cognitive radio cooperative spectrum sensing." Thesis, California State University, Long Beach, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10252432.
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<p> The effectiveness of a cognitive radio (CR) system depends mainly on involved spectrum sensing techniques. The main aim of CR is for effective utilization of the spectrum opportunistically by sharing it with secondary users (SUs), when the primary user (PU) is absent. In this project, cooperative spectrum sensing using weights based on the distance measures from the PU and Multitaper Method (MTM) method is briefly explained. The results show that MTM method provides more accurate threshold value compared to other methods for low signal to noise ratios (SNRs), hence improving the spectrum sensing technique. The results also show that MTM method requires a lesser number of Orthogonal Frequency Division Multiplexing (OFDM) sub-blocks compared to Periodogram (PE) for the same performance.</p>
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Kataria, Amit. "Cognitive radios spectrum sensing issues /." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/5047.
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Thesis (M.S.)--University of Missouri-Columbia, 2007.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 28, 2008) Includes bibliographical references.
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Bokharaiee, Najafee Simin. "Spectrum Sensing in Cognitive Radio Networks." IEEE Transactions on Vehicular Technology, 2011. http://hdl.handle.net/1993/24069.
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Given the ever-growing demand for radio spectrum, cognitive radio has recently emerged as an attractive wireless communication technology. This dissertation is concerned with developing spectrum sensing algorithms in cognitive radio networks where a single or multiple cognitive radios (CRs) assist in detecting licensed primary bands employed by single or multiple primary users.
First, given that orthogonal frequency-division multiplexing (OFDM) is an important
wideband transmission technique, detection of OFDM signals in low-signal-to-noise-ratio scenario is studied. It is shown that the cyclic prefix correlation coefficient (CPCC)-based spectrum sensing algorithm, which was previously introduced as a simple and computationally efficient spectrum-sensing method for OFDM signals, is a special case of the constrained generalized likelihood ratio test (GLRT) in the absence of multipath.
The performance of the CPCC-based algorithm degrades in a multipath scenario. However when OFDM is implemented, by employing the inherent structure of OFDM signals and exploiting multipath correlation in the GLRT algorithm a simple and low-complexity algorithm called the multipath-based constrained-GLRT (MP-based C-GLRT) algorithm is obtained. Further performance improvement is achieved by combining both the CPCC- and MP-based C-GLRT algorithms. A simple GLRT-based detection algorithm is also developed for unsynchronized OFDM signals.
In the next part of the dissertation, a cognitive radio network model with multiple CRs is considered in order to investigate the benefit of collaboration and diversity in improving the overall sensing performance. Specially, the problem of decision fusion for cooperative spectrum sensing is studied when fading channels are present between the CRs and the fusion center (FC). Noncoherent transmission schemes with on-off keying (OOK) and binary frequency-shift keying (BFSK) are employed to transmit the binary decisions to the FC. The aim is to maximize the achievable secondary throughput of the CR network.
Finally, in order to reduce the required transmission bandwidth in the reporting phase of the CRs in a cooperative sensing scheme, the last part of the dissertation examines nonorthogonal transmission of local decisions by means of on-off keying. Proposed and analyzed is a novel decoding-based fusion rule for combining the hard decisions in a linear manner.
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Malafaia, Daniel Filipe Simões. "Wideband spectrum sensing for cognitive radio." Doctoral thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/21779.
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Doutoramento em Telecomunicações<br>Esta tese tem como objetivo o desenvolvimento de uma unidade autónoma
de deteção espetral em tempo real. Para tal são analisadas várias implementações
para a estimação do nível de ruído de fundo de forma a se poder
criar um limiar adaptativo para um detetor com uma taxa constante de falso
alarme. Além da identificação binária da utilização do espetro, pretende-se
também obter a classificação individual de cada transmissor e a sua ocupação
ao longo do tempo. Para tal são exploradas duas soluções baseadas
no algoritmo, de agrupamento de dados, conhecido como maximização de
expectativas que permite identificar os sinais analisados pela potência recebida
e relação de fase entre dois recetores. Um algoritmo de deteção
de sinal baseado também na relação de fase de dois recetores e sem necessidade
de estimação do ruído de fundo é também demonstrado. Este
algoritmo foi otimizado para permitir uma implementação eficiente num arranjo
de portas programáveis em campo a funcionar em tempo real para
uma elevada largura de banda, permitindo também estimar a direção da
transmissão detetada.<br>The purpose of this thesis is to develop an autonomous unit for real time
spectrum sensing. For this purpose, several implementations for the estimation
of the background noise level are analysed to create an adaptive
threshold and ensure a constant false alarm rate detector. In addition to
the binary identification of the spectrum usage, it is also intended to obtain
an individual classification of each transmitter occupation and its spectrum
usage over time. To do so, two solutions based on the expectation maximization
data clustering, that allow to identify the analyzed signals by the
received power and phase relation between two receivers, were explored. A
signal detection algorithm, also based on the phase relationship between
two receivers and with no need for noise estimation is also demonstrated.
This algorithm has been optimized to allow an efficient implementation in
a FPGA operating in real time for a high bandwidth and it also allows the
estimation of the direction of arrival of the detected transmission.
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Prawatmuang, Warit. "Cooperative spectrum sensing for cognitive radio." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/cooperative-spectrum-sensing-for-cognitive-radio(db1dd626-841e-4b05-b5af-fa27e59c63bb).html.
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Cognitive Radio (CR) aims to access the wireless spectrum in an opportunistic manner while the licensed user is not using it. To accurately determine the licensed user's existence, spectrum sensing procedure is vital to CR system. Energy detection-based spectrum sensing techniques is favourable due to its simplicity and low complexity. In addition, to improve the detection performance, cooperative spectrum sensing technique exploits multi-user diversity and mitigates detection uncertainty. In this thesis, we investigate several energy detection based cooperative spectrum sensing techniques.First, the closed-form analysis for the Equal Gain Combining based Soft Decision Combining (EGC-SDC) scheme, in which all CR users forward its observation to the fusion center, is derived. In order to reduce the communication overhead between CR users and the fusion center, we proposed quantized cooperative spectrum sensing technique, in which CR users quantize its local observation before forwarding to the fusion center. Next, the Double Threshold scheme, where some users only forward its local decision while other users forward its observation, is considered and analyzed. To further reduce the communication overhead, we also proposed that quantization is applied to the users who forward its observation. Later on, three sequential cooperative spectrum sensing schemes in time-varying channel are considered. By aggregating past local observations from previous sensing slots, CR users can improve the detection performance. The Weighted Sequential Energy Detector (SED) scheme simply takes fixed number of past local observations, while the other two schemes, Two-Stage SED and Differential SED, adaptively determine the number of observations, based on its decision towards primary user's existence.Simulation results show that the analysis on EGC-SDC scheme is accurate and the quantized cooperative spectrum sensing technique can improve the performance and approach the detection performance of EGC-SDC scheme with much less bandwidth requirement. Also, the Double Threshold scheme can help improve the detection performance over the conventional technique. Furthermore, the analysis on Double Threshold provides a closed-form for the probability of false alarm and detection. Additionally, the sequential spectrum sensing schemes are shown to improve the detection performance and enable CR system to work in scenarios that the conventional technique can not accommodate.
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Valieva, Inna. "Spectrum Sensing for Dynamic Spectrum Access in Cognitive Radio." Licentiate thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-52881.
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Abstract. The number of mobile devices is constantly growing, and the exclusivestatic spectrum allocation approach is leading to the spectrum scarcity problem whensome of the licensed bands are heavily occupied and others are nearly unused.Spectrum sharing and opportunistic spectrum access allow achieving more efficientspectrum utilization. Radio scene analysis is a first step in the cognitive radiooperation required to employ opportunistic spectrum access scenarios such as thedynamic spectrum access or frequency hopping spread spectrum. The objective of thiswork is to develop and virtual prototype the subset of radio scene analysis algorithmsintended to be used for deployment of opportunistic spectrum access in our targetapplication: a cognitive radio network consisting of multiple software-defined radionodes BitSDR. The proposed radio scene analysis algorithms are devoted to solvingtwo radio scene analysis problems: 1. detection of vacant frequency channels toimplement spectrum sharing scenarios; 2. waveform estimation including modulationtype, symbol rate, and central frequency estimation. From the subset of two radioscene analysis problems two hypotheses are formulated: the first is related to thevacant band identification and the second to waveform estimation. Then sevenresearch questions related to the trade-off between the sensing accuracy and real-time operation requirement for the proposed radio scene analysis algorithms, the nature of the noise, and assumptions used to model the radio scene environment such as the AWGN channel. In the scope of this work, Hypothesis 1, dedicated to vacant frequency band detection, has been proven. Research questions related to the selection of the observation bandwidth, vacant channels detection threshold, and the optimal algorithm have been answered. We have proposed, prototyped, and tested a vacant frequency channels detection algorithm based on wavelet transform performing multichannel detection in the wide band of 56 MHz based on the received signal observed during500 microseconds. Detection accuracy of 91 % has been demonstrated. Detection has been modeled as a binary hypothesis testing problem. Also, energy detection and cyclostationary feature extraction algorithms have been prototyped and tested, however, they have shown lower classification accuracy than wavelets. Answering research question 7 revealed the advantage of using wavelets due to the potential of the results of wavelet transform to be applied for solving the waveform estimation problem including symbol rate and modulation type. Test data samples have been generated during the controlled experiment by the hardware signal generator and received by proprietary hardware based on AD9364 Analog Devices transceiver. To test Hypothesis 2 research questions related to the waveform estimation have been elaborated. We could not fully prove Hypothesis 2 in the scope of this work. The algorithm and features that have been chosen for modulation type classification have not met the required classification accuracy to classify between five studied modulation classes 2FSK, BPSK, QPSK, 8PSK, and 16PSK. To capture more of the fine differences between the received signal modulated into different linear modulations it has been suggested to use the spectral features derived from the time-series signal observed during 500 microseconds or less observation time in the scope of the future work. However, the binary classification between 2FSK and BPSKpresented in Paper 1 could be performed based on instantaneous values and SNRinput: ensemble boosted trees and decision trees have shown an average classification accuracy of 86.3 % and 86.0 % respectively and classification speed of 1200000objects per second, what is faster than required 2000 objects per second.3The prototyping and testing of the proposed algorithm for symbol rate estimation based on deep learning have been performed to answer research question 2. Wavelet transform feature extraction has been proposed to be applied as a preprocessing step for deep learning-based estimation of the symbol rate for 2FSK modulated signals. This algorithm has shown an improvement in the accuracy of the symbol rate estimation in comparison with cyclostationary based detection. The validation accuracy of the symbol rate classification has reached 99.7 %. During testing, the highest average classification accuracy of 100 % has been observed for the signals with SNR levels 25-30 dB, while for signals with SNR 20-25 dB it was 96.3 %.
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Kaligineedi, Praveen. "Cooperative spectrum sensing for cognitive radio networks." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/30261.
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Radio spectrum is a very scarce and important resource for wireless communication systems. However, a recent study conducted by Federal Communications Commission (FCC) found that most of the currently allocated radio spectrum is not efficiently utilized by the licensed primary users. Granting opportunistic access of the spectrum to unlicensed secondary users has been suggested as a possible way to improve the utilization of the radio spectrum. Cognitive Radio (CR) is an emerging technology that would allow an unlicensed (cognitive) radio to sense and efficiently use any available spectrum at a given time. Reliable detection of the primary users is an important task for CR systems. Cooperation among a few sensors can offer significant gains in the performance of the CR spectrum sensing system by countering shadow-fading effects. In this thesis, we consider a parallel fusion based cooperative sensing network, in which the sensors send their sensing information to an access point, which makes the final decision regarding presence or absence of the primary signal. We assume that energy detection is used at each sensor. Presence of few malicious users sending false sensing data can severely degrade the performance of such a cooperative sensing system. In this thesis, we investigate schemes to identify malicious users based on outlier detection techniques. We take into consideration constraints imposed by the CR scenario, such as limited information about the primary signal propagation environment and small sensing data sample size. Considering partial knowledge of the primary user activity, we propose a novel method to identify malicious users. We further propose malicious user detection schemes that take into consideration the spatial location of the sensors. We then investigate efficient sensor allocation and quantization techniques for a CR network operating in multiple primary bands. We explore different methods to assign CR sensors to various primary bands. We then study efficient single-bit quantization schemes at the sensors. We show that the optimal quantization scheme is, in general, non-convex and propose a suboptimal solution based on a convex restriction of the original problem. We compare the performance of the proposed schemes using simulations.
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Vakili, Arash. "Adaptive spectrum sensing for cognitive radio networks." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106425.
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Spectrum sensing is an important functionality of cognitive radio as a means to detect the presence or absence of the primary user (PU) in a certain spectrum band. Energy detection is a widely used spectrum sensing technique based on the assumption that the PU is either present or absent during the whole sensing period. However, this assumption is not realistic in a dynamic environment where the PU could appear or disappear at any time. The performance of the conventional energy detector (ED) actually deteriorates in the scenario where the PU activity status changes during the sensing period.Therefore, it is crucial to design a detector which can adapt to such an environment and reliably detect a change in the PU activity. Several sequential change detection techniques already exist in the literature; however, change detection in a fixed sensing duration has not been given enough attention. In this dissertation, three adaptive EDs are proposed to improve the detection performance in dynamic environments, where there is a single change in the PU activity during a fixed sensing period. In particular, we address the change detection problem using an exponential weighting approach and two theoretical approaches based on the composite hypothesis testing. In the first case, an intuitive idea of exponential weighting of the received energies is applied to design an adaptive ED that aims to satisfy the Neyman-Pearson (NP) criterion. The performance analysis and simulation results prove that the proposed adaptive ED outperforms the conventional ED and also the only existing adaptive ED in the literature that deals with the aforementioned issue. In the second case, two theoretical approaches based on the composite hypothesis testing are used to design two additional adaptive EDs that improve the change detection during the sensing period. The first approach, known as the generalized likelihood ratio test (GLRT), uses the maximum likelihood estimation (MLE) of the unknown change location in a likelihood ratio test. In this case, an iterative method is proposed to reduce the computational complexity of the MLE process. The second approach, referred to as composite-Bayesian, assumes that the unknown change location is a discrete random variable whose probability mass function (PMF) is available. The PU channel access pattern is modelled as a two-state Markov chain to obtain the PMF of the change location and the probability of occurrence of the two hypotheses. The resultant adaptive ED based on the GLRT approach aims to satisfy the NP criterion while the adaptive ED based on the composite-Bayesian approach aims to minimize the probability of error. It is demonstrated through simulations that these two proposed adaptive EDs have superior performance over the conventional ED. Furthermore, the GLRT-based adaptive ED outperforms the first proposed adaptive ED based on the exponential weighting approach.<br>La détection de spectre est une fonctionnalité importante de la radio cognitive car elle permet de vérifier la présence ou l'absence d'un utilisateur principal (PU) sur une bande de spectre donnée. La détection de l'énergie est une méthode fréquemment utilisée pour y parvenir.Cette dernière s'appuie sur l'hypothèse que le PU est présent ou absent pour la totalité de la période de mesure. Cependant, cette hypothèse n'est pas réaliste pour un environnement dynamique dans lequel le PU peut apparaître ou disparaître à n'importe quel instant. En effet, les performances d'un détecteur d'énergie conventionnel (ED) se détériorent lorsque l'état du PU varie au cours de la période durant laquelle les mesures sont effectuées. C'est donc pour cette raison qu'il est nécessaire de concevoir un détecteurqui s'adapte bien à ce genre d'environnement et qui permet de détecter de manière fiable tout changement dans l'activité du PU. Plusieurs techniques de détection de changements séquentiels existent dans la littérature mais la détection de changement pour une durée fixe n'a pas été explorée suffisamment en détails. Dans le cadre de ce mémoire, trois EDs adaptatifs sont proposés dans le but d'améliorer les performances dans un environnement dynamique au sein duquel il y a un seul changement au niveau de l'activité du PU et ce durant une période de mesure de durée fixe. Pour tenter de résoudre cette problématique, une approche à pondération exponentielle et deux approches théoriques en lien avec le test d'hypothèse composée sont proposées. Dans le premier cas, une approche intuitive exploitant la pondération exponentielle de l'énergie mesurée est utilisée afin de concevoir un ED adaptatif qui satisfait le critère de Neyman-Pearson (NP). L'analyse des performances et des résultats de simulation prouvent que cette stratégie offre de meilleures performances par rapport aux ED conventionnels. Il s'agit également du seul ED adaptatif présent dans la littérature qui tente de résoudre la problématique précédemment mentionnée. Dans le second cas, deux approches théoriques fondées sur le test d'hypothèse composée sont utilisées afin de concevoir deux nouveaux EDs adaptatifs qui améliorent la détection de changements durant la période de mesure. La première approche s'appuie sur le test généralisé de vraisemblance (GLRT) et utilise une estimation de la vraisemblance maximale (MLE) de la position inconnue du changement. Dans ce cas, une méthode itérative est proposée pour réduire la complexité de calcul du processus de MLE. La deuxième approche, dite composée bayésienne, prend pour acquis que la position inconnue du changement est une variable aléatoire discrète dont la loi de probabilité (PMF) est connue. Pour cette dernière approche, les accès au canal sont modélisés par un modèle de Markov à deux états afin d'obtenir la PMF de la position du changement et la probabilité d'occurrence des deux hypothèses. Le ED adaptatif utilisant le GLRT tente de satisfaire le critère de NP tandis que le ED adaptatif utilisant l'approche de la composée bayésienne tente de minimiser la probabilité d'une erreur. Il est démontré à l'aide de simulations que ces deux EDs adaptatifs offrent des performances supérieures à celles du ED conventionnel. En outre, le ED adaptatif utilisant le GLRT surpasse le ED adaptive utilisant l'approche pondération exponentielle.
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Christiansen, Jørgen Berle. "Distribution Based Spectrum Sensing in Cognitive Radio." Thesis, Norwegian University of Science and Technology, Department of Electronics and Telecommunications, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10844.
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<p>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.</p>
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Sun, Hongjian. "Collaborative spectrum sensing in cognitive radio networks." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/4879.
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The radio frequency (RF) spectrum is a scarce natural resource, currently regulated by government agencies. With the explosive emergence of wireless applications, the demands for the RF spectrum are constantly increasing. On the other hand, it has been reported that localised temporal and geographic spectrum utilisation efficiency is extremely low. Cognitive radio is an innovative technology designed to improve spectrum utilisation by exploiting those spectrum opportunities. This ability is dependent upon spectrum sensing, which is one of most critical components in a cognitive radio system. A significant challenge is to sense the whole RF spectrum at a particular physical location in a short observation time. Otherwise, performance degrades with longer observation times since the lagging response to spectrum holes implies low spectrum utilisation efficiency. Hence, developing an efficient wideband spectrum sensing technique is prime important. In this thesis, a multirate asynchronous sub-Nyquist sampling (MASS) system that employs multiple low-rate analog-to-digital converters (ADCs) is developed that implements wideband spectrum sensing. The key features of the MASS system are, 1) low implementation complexity, 2) energy-efficiency for sharing spectrum sensing data, and 3) robustness against the lack of time synchronisation. The conditions under which recovery of the full spectrum is unique are presented using compressive sensing (CS) analysis. The MASS system is applied to both centralised and distributed cognitive radio networks. When the spectra of the cognitive radio nodes have a common spectral support, using one low-rate ADC in each cognitive radio node can successfully recover the full spectrum. This is obtained by applying a hybrid matching pursuit (HMP) algorithm - a synthesis of distributed compressive sensing simultaneous orthogonal matching pursuit (DCS-SOMP) and compressive sampling matching pursuit (CoSaMP). Moreover, a multirate spectrum detection (MSD) system is introduced to detect the primary users from a small number of measurements without ever reconstructing the full spectrum. To achieve a better detection performance, a data fusion strategy is developed for combining sensing data from all cognitive radio nodes. Theoretical bounds on detection performance are derived for distributed cognitive radio nodes suffering from additive white Gaussian noise (AWGN), Rayleigh fading, and log-normal fading channels. In conclusion, MASS and MSD both have a low implementation complexity, high energy efficiency, good data compression capability, and are applicable to distributed cognitive radio networks.
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Axell, Erik. "Topics in Spectrum Sensing for Cognitive Radio." Licentiate thesis, Linköping : Department of Electrical Engineering, Linköping University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51748.
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Mariani, Andrea <1984>. "Spectrum Sensing Algorithms for Cognitive Radio Applications." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5615/.
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Future wireless communications systems are expected to be extremely dynamic, smart and capable to interact with the surrounding radio environment. To implement such advanced devices, cognitive radio (CR) is a promising paradigm, focusing on strategies for acquiring information and learning. The first task of a cognitive systems is spectrum sensing, that has been mainly studied in the context of opportunistic spectrum access, in which cognitive nodes must implement signal detection techniques to identify unused bands for transmission.
In the present work, we study different spectrum sensing algorithms, focusing on their statistical description and evaluation of the detection performance. Moving from traditional sensing approaches we consider the presence of practical impairments, and analyze algorithm design. Far from the ambition of cover the broad spectrum of spectrum sensing, we aim at providing contributions to the main classes of sensing techniques. In particular, in the context of energy detection we studied the practical design of the test, considering the case in which the noise power is estimated at the receiver. This analysis allows to deepen the phenomenon of the SNR wall, providing the conditions for its existence and showing that presence of the SNR wall is determined by the accuracy of the noise power estimation process. In the context of the eigenvalue based detectors, that can be adopted by multiple sensors systems, we studied the practical situation in presence of unbalances in the noise power at the receivers. Then, we shift the focus from single band detectors to wideband sensing, proposing a new approach based on information theoretic criteria. This technique is blind and, requiring no threshold setting, can be adopted even if the statistical distribution of the observed data in not known exactly. In the last part of the thesis we analyze some simple cooperative localization techniques based on weighted centroid strategies.
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Simpson, Oluyomi. "Optimal cooperative spectrum sensing for cognitive radio." Thesis, University of Hertfordshire, 2016. http://hdl.handle.net/2299/17246.
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The rapid increasing interest in wireless communication has led to the continuous development of wireless devices and technologies. The modern convergence and interoperability of wireless technologies has further increased the amount of services that can be provided, leading to the substantial demand for access to the radio frequency spectrum in an efficient manner. Cognitive radio (CR) an innovative concept of reusing licensed spectrum in an opportunistic manner promises to overcome the evident spectrum underutilization caused by the inflexible spectrum allocation. Spectrum sensing in an unswerving and proficient manner is essential to CR. Cooperation amongst spectrum sensing devices are vital when CR systems are experiencing deep shadowing and in a fading environment. In this thesis, cooperative spectrum sensing (CSS) schemes have been designed to optimize detection performance in an efficient and implementable manner taking into consideration: diversity performance, detection accuracy, low complexity, and reporting channel bandwidth reduction. The thesis first investigates state of the art spectrums sensing algorithms in CR. Comparative analysis and simulation results highlights the different pros, cons and performance criteria of a practical CSS scheme leading to the problem formulation of the thesis. Motivated by the problem of diversity performance in a CR network, the thesis then focuses on designing a novel relay based CSS architecture for CR. A major cooperative transmission protocol with low complexity and overhead - Amplify and Forward (AF) cooperative protocol and an improved double energy detection scheme in a single relay and multiple cognitive relay networks are designed. Simulation results demonstrated that the developed algorithm is capable of reducing the error of missed detection and improving detection probability of a primary user (PU). To improve spectrum sensing reliability while increasing agility, a CSS scheme based on evidence theory is next considered in this thesis. This focuses on a data fusion combination rule. The combination of conflicting evidences from secondary users (SUs) with the classical Dempster Shafter (DS) theory rule may produce counter-intuitive results when combining SUs sensing data leading to poor CSS performance. In order to overcome and minimise the effect of the counter-intuitive results, and to enhance performance of the CSS system, a novel state of the art evidence based decision fusion scheme is developed. The proposed approach is based on the credibility of evidence and a dissociability degree measure of the SUs sensing data evidence. Simulation results illustrate the proposed scheme improves detection performance and reduces error probability when compared to other related evidence based schemes under robust practcial scenarios. Finally, motivated by the need for a low complexity and minmum bandwidth reporting channels which can be significant in high data rate applications, novel CSS quantization schemes are proposed. Quantization methods are considered for a maximum likelihood estimation (MLE) and an evidence based CSS scheme. For the MLE based CSS, a novel uniform and optimal output entropy quantization scheme is proposed to provide fewer overhead complexities and improved throughput. While for the Evidence based CSS scheme, a scheme that quantizes the basic probability Assignment (BPA) data at each SU before being sent to the FC is designed. The proposed scheme takes into consideration the characteristics of the hypothesis distribution under diverse signal-to-noise ratio (SNR) of the PU signal based on the optimal output entropy. Simulation results demonstrate that the proposed quantization CSS scheme improves sensing performance with minimum number of quantized bits when compared to other related approaches.
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Wang, Jun. "Multitaper spectrum based detection for spectrum sensing in cognitive radio networks /." access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?mphil-ee-b23750480f.pdf.
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Thesis (M.Phil.)--City University of Hong Kong, 2009.<br>"Submitted to Department of Electronic Engineering in partial fulfillment of the requirements for the degree of Master of Philosophy." Includes bibliographical references (leaves 66-74)
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Duarte, Miguel Filipe Batista. "Spectrum sensing through software defined radio." Master's thesis, Faculdade de Ciências e Tecnologia, 2014. http://hdl.handle.net/10362/12293.
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Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia<br>A change in paradigm when it comes to controlling radio transmissions is in course.
Tasks usually executed in an exclusive class of hardware systems are increasingly controlled by software systems. A deep change to the software domain is foreseeable, creating a true Software Defined Radio. At the same time this change occurs, the radioelectric spectrum is almost completely licensed. However, the spectrum is rarely used to its full extent over time, enabling its opportunistic use while the licensed devices do not communicate.
This is a part of the notion of Cognitive Radio, a new kind of radio capable of using
the spectrum in an opportunistic way. These two new paradigms in radio access can be
combined to produce a exible and reliable radio, overcoming the issues with radioelectric spectrum scarcity. This dissertation starts an exploration in this area by combining these two paradigms through the use of an Energy Detector implemented in a Universal Software Radio Peripheral device and using the GNURadio suite. The performance of such a system is tested by calculating the Probabilities of Detection and False Alarm in real scenarios
and comparing them to the expected theoretical values. A method for defining thresholds for narrowband signals is also tested based on works in Information Theory concepts, i.e.,the Akaike Information Criteria and the Minimum Description Length. The results are tested for a real transmission using two USRP platforms communicating with each other,one acting as the licensed user and the other acting as the secondary, opportunistic user.
Finally, we highlight the technological work developed in this dissertation, which may
support future research works through the use of the developed scripts, allowing a faster method to test algorithms with different parameterization.
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Zhou, Xiangwei. "Efficient spectrum sensing and utilization for cognitive radio." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42714.
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Cognitive radio (CR) technology has recently been introduced to opportunistically exploit the spectrum. We present a robust and cost-effective design to ensure the improvement of spectrum efficiency with CR. We first propose probability-based spectrum sensing by utilizing the statistical characteristics of licensed channel occupancy, which achieves nearly optimal performance with relatively low complexity. Based on the statistical model, we then propose periodic spectrum sensing scheduling to determine the optimal inter-sensing duration and vary the transmit power at each data sample to enhance throughput and reduce interference. We further develop a probability-based scheme for combination of local sensing information collected from cooperative CR users, which enables combination of both synchronous and asynchronous sensing information. To satisfy the stringent bandwidth constraint for reporting, we also propose to simultaneously send local sensing data to a combining node through the same narrowband channel. With proper preprocessing at individual users, such a design maintains reasonable detection performance while the bandwidth required for reporting does not change with the number of cooperative users. To better utilize the spectrum and avoid possible interference, we propose spectrum shaping schemes based on spectral precoding, which enable efficient spectrum sharing between CR and licensed users and exhibit the advantages of both simplicity and flexibility. We also propose a novel resource allocation approach based on the probabilities of licensed channel availability obtained from spectrum sensing. Different from conventional approaches, the probabilistic approach exploits the flexibility of CR to ensure efficient spectrum usage and protect licensed users from unacceptable interference.
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Cardenas, Juarez Marco Aurelio. "Spectrum sensing and throughput analysis for cognitive radio." Thesis, University of Leeds, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.582744.
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Cognitive radio (CR) technology offers an innovative solution to improve spectrum efficiency, thus satisfying the greater demand to be placed on the electromagnetic spectrum by future wireless networks and communications. In this sense, the ulti- mate purpose of the spectrum sensing feature in cognitive radio is to determine the absence or presence of licensed users' signals in a frequency band of interest. More- over, due to the wide variety of scenarios in which cognitive radios may operate and the random nature of wireless channels, spectrum sensing algorithms are expected to perform well at a very low signal-to-noise-ratio (SNR), thus playing not only an important but also a very challenging role in CR. In this thesis, locally optimum (LO) detection, (known to be optimum at low SNR), is adopted in the design of blind and semi-blind detection algorithms by fo- cusing on linear modulation in the presence of an unknown phase shift and additive white Gaussian noise. The proposed LO detectors are shown to significantly out- perform the energy detector in the case of BPSK signals and to be less sensitive to noise power mismatch whilst their complexity is only slightly higher than that of the energy detector. Furthermore, the spectrum sensing performance is improved by taking advantage of the spatial diversity gained through cooperation. In addition, next generation wireless networks will need higher data rates to meet the requirements of the expected customers and services. The spectrum sen- sing duration and the secondary user's achievable throughput trade-off problem is addressed here by allowing the constraint on the probability of detection to be in outage with a specified percentage, taking into account the detrimental effect of unknown channel gains over different fading conditions in centralised cooperative networks, which is a more realistic scenario. The spectrum sensing time/secondary user's achievable throughput trade-off is then formulated and optimised accordingly.
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Wang, Lingfeng. "Cognitive radio-enabled spectrum sensing in radar systems." Thesis, University of Bristol, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509771.
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Miar, Yasin. "Improved Wideband Spectrum Sensing Methods for Cognitive Radio." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23333.
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Abstract Cognitive Radio (CR) improves the efficiency of spectrum utilization by allowing non- licensed users to utilize bands when not occupied by licensed users. In this thesis, we address several challenges currently limiting the wide use of cognitive radios. These challenges include identification of unoccupied bands, energy consumption and other technical challenges. Improved accuracy edge detection techniques are developed for CR to mitigate both noise and estimation error variance effects. Next, a reduced complexity Simplified DFT (SDFT) is proposed for use in CR. Then, a sub-Nyquist rate A to D converter is introduced to reduce energy consumption. Finally, a novel multi-resolution PSD estimation based on expectation-maximization algorithm is introduced that can obtain a more accurate PSD within a specified sensing time.
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Huang, Qi. "Robust spectrum sensing techniques for cognitive radio networks." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22012.
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Cognitive radio is a promising technology that improves the spectral utilisation by allowing unlicensed secondary users to access underutilised frequency bands in an opportunistic manner. This task can be carried out through spectrum sensing: the secondary user monitors the presence of primary users over the radio spectrum periodically to avoid harmful interference to the licensed service. Traditional energy based sensing methods assume the value of noise power as prior knowledge. They suffer from the noise uncertainty problem as even a mild noise level mismatch will lead to significant performance loss. Hence, developing an efficient robust detection method is important. In this thesis, a novel sensing technique using the F-test is proposed. By assuming a multiple antenna assisted receiver, this detector uses the F-statistic as the test statistic which offers absolute robustness against the noise variance uncertainty. In addition, since the channel state information (CSI) is required to be known, the impact of CSI uncertainty is also discussed. Results show the F-test based sensing method performs better than the energy detector and has a constant false alarm probability, independent of the accuracy of the CSI estimate. Another main topic of this thesis is to address the sensing problem for non-Gaussian noise. Most of the current sensing techniques consider Gaussian noise as implied by the central limit theorem (CLT) and it offers mathematical tractability. However, it sometimes fails to model the noise in practical wireless communication systems, which often shows a non-Gaussian heavy-tailed behaviour. In this thesis, several sensing algorithms are proposed for non-Gaussian noise. Firstly, a non-parametric eigenvalue based detector is developed by exploiting the eigenstructure of the sample covariance matrix. This detector is blind as no information about the noise, signal and channel is required. In addition, the conventional energy detector and the aforementioned F-test based detector are generalised to non-Gaussian noise, which require the noise power and CSI to be known, respectively. A major concern of these detection methods is to control the false alarm probability. Although the test statistics are easy to evaluate, the corresponding null distributions are difficult to obtain as they depend on the noise type which may be unknown and non-Gaussian. In this thesis, we apply the powerful bootstrap technique to overcome this difficulty. The key idea is to reuse the data through resampling instead of repeating the experiment a large number of times. By using the nonparametric bootstrap approach to estimate the null distribution of the test statistic, the assumptions on the data model are minimised and no large sample assumption is invoked. In addition, for the F-statistic based method, we also propose a degrees-of-freedom modification approach for null distribution approximation. This method assumes a known noise kurtosis and yields closed form solutions. Simulation results show that in non-Gaussian noise, all the three detectors maintain the desired false alarm probability by using the proposed algorithms. The F-statistic based detector performs the best, e.g., to obtain a 90% detection probability in Laplacian noise, it provides a 2.5 dB and 4 dB signal-to-noise ratio (SNR) gain compared with the eigenvalue based detector and the energy based detector, respectively.
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Claudino, Lucas dos Santos Araujo. "Cognitive radio : spectrum sensing and optimal resource allocation." Universidade Estadual de Londrina. Centro de Tecnologia e Urbanismo. Programa de Pós-Graduação em Engenharia Elétrica, 2018. http://www.bibliotecadigital.uel.br/document/?code=vtls000218384.
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Redes de Rádio Cognitivo constituem uma tecnologia recente que busca fazer o uso otimizado do espectro de frequências, que jáestá super-utilizado. Essa tecnologia oferece meios para portadores de licensa compartilhar sua banda de frequências com outros usuários para possibilitar o uso eficiente e também recerber certos benefícios em troca, como descontos ou aumento do tempo de validade da licensa. Dado que essa tecnologia é relativamente recente, os seus estudos estão ainda incompletos e precisam de uma pesquisa mais aprofundada. A fim de contribuir com a literatura, este trabalho trás importantes contrubuições para diferentes áreas do Rádio Cognitivo. Primeiramente, várias técnicas de sensoriamento espectral como Filtro Casado, Sensor de Energia, Sensor de Razão de Hadamard ou Sensor de Valor Absoluto de Covariância são analisados. Todos esses detectores são estudados e comparados de modo a oferecer ao leitor uma ampla visão sobre todas suas características, fraquezas e pontos fortes. Após esse estudo, o sensor mais promissor é escolhido para ser aplicado em um cenário realístico de transmissão sem fio. Escolheu-se o sensor de Razão de Hadamard, dado sua capacidade de prover altas taxas de detecção, baixa detecção errada ou alarme falso com um número necessário de amostras relativamente baixo. A segunda parte desta Dissertação é baseada em técnicas de otimização não linear que buscam maximizar a soma das capacidades de uma rede de rádio cognitivo. Uma MISO-CRN (Multiple-Input Multiple-Output Cognitive Radio Network) foi escolhida como arquitetura de aplicação e sua otimização foi divida em duas partes: cancelamento de interferência e alocação de potência. Essa técnica é conhecida como Zero Forcing-Water Filing, e alcança a capacidade máxima de transmissão sob certas configurações de sistema e canal. Além disso, esta pesquisa desenvolveu também uma aproximação prática para encontrar o número ótimo de usuários secundários ativos que proporcione a máxima capacidade da rede. Essa é uma ferramenta muito útil, uma vez que pode prover uma maneira simples de escolha do número permitido de usuários secundários para um certo cenário. Finalmente, técnicas de estimativa de canal aplicadas a redes de rádio cognitivo são estudadas. A transmissão completa em banda-base equivalente é descrita, a qual inclui a transmissão de sequência piloto, a estimativa da matriz de canal e uso dessa estimativa para gerar a matriz ótima de precodificação. Além disso, analisou-se o efeito da estimativa imperfeita do canal no sistema de transmissão com precodificação, na tentativa de se encontrar técnicas para superar esses problemas e melhorar o desempenho do sistema de comunicação com múltiplas antenas.<br>Cognitive Radio Network is a recent and emerging technology that aims to optimally use the already overcrowded frequency spectrum. This technology offers means for license-holders to share their spectrum bandwidth with other users, in order to make an efficient use of it and also receive some benefits, like payback or increase in license time. Once this is a recent technology, many studies are still incomplete and need furthers research. Ir order to contribute with the literature, this work brings some important research of a few different parts of Cognitive Radio. Firstly, various spectrum sensing techniques, such as Matched Filter, Energy Sensing, Hadamard Ratio Sensor or Covariance Absolute Value Detector are analyzed. All those sensors are studied and compared, in order to give a broad overview about its characteristics, strengths and weaknesses. After this study, the most promising detector is chosen to be applied into realistic wireless channels. The Hadamard Ratio Sonsor has been chosen, given its capacity of providing high detection rates, low miss detection or false alarm with a relatively low number of samples. The second part is based on non-linear optimization techniques and aims to maximize the sum capacity of a cognitive radio network. A MISO-CRN was chosen as target architecture, and the optimization was divided into two parts: power allocation and interference nulling. This technique is known as Zero Forcing-Water Filing strategy, which achieves maximum sum capacity under certain system and channel configurations. Also, this research came up with a practical approximation to find out the optimum number of active secondary users to achieve maximum capacity. This is a very useful tool, once it can provide an easy way of choosing the allowed number of SUs for a given configuration of number of antennas at the base station and link quality (related to signal to interference plus noise ratio). Finally, channel estimation techniques applied to cognitive radio networks are analyzed. A complete base-band transmission is described, which includes pilot sequence transmission, channel matrix estimation and optimal precoder matrix generation based on channel estimative. Also, the effect of imperfect channel estimation has been studied to provide means of developing techniques to overcome possible problems and enhance the MIMO communication performance
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Teguig, Djamel. "Cooperative Spectrum Sensing Algorithms For Cognitive Radio Networks." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/219942.
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The work presented in this thesis concerns one of the key enabling techniques related to cognitive radio functionalities which is spectrum sensing as well as cooperative spectrum sensing. As cooperative spectrum sensing (CSS) approaches are commonly used for combating fading and improving detection performance, their performances using different combining rules have been analyzed. Due to the low implementation complexity, Goodness of Fit based spectrum sensing has been studied for cognitive radio applications. Motivated by its nice features of local sensing, a distributed consensus spectrum sensing for CR, has been presented, integrating a Goodness of Fit based spectrum sensing scheme.<br>Le travail présenté dans cette thèse concerne l'une des techniques clés dans les fonctionnalités de la radio cognitive qui est la détection du spectre ainsi que la détection coopérative du spectre. La détection coopérative est couramment utilisée pour la lutte contre l’évanouissement du canal à fin d'améliorer les performances de la détection. Les performances de la détection coopérative en utilisant différentes règles de fusion ont été analysées. En raison sa simplicité, la détection du spectre par les testes d’adéquation a été étudiée pour les applications de la radio cognitive. Motivé par la caractéristique d’être indépendant de bruit, ces testes d’adéquation ont été utilisés pour la détection locale, pour la détection coopérative distribuée.<br>Doctorat en Sciences de l'ingénieur et technologie<br>info:eu-repo/semantics/nonPublished
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sajiduet84@gmail, com Sajid Mahmood /., and abdullah@gmail com Mujeeb Abdullah /. mujeeb. "Priority Queuing Based Spectrum sensing Methodology in Cognitive Radio Network." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3219.
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Radio spectrum is becoming scarce resource due to increase in the usage of wireless communication devices. However studies have revealed that most of the allotted spectrum is not used effectively. Given the demand for more bandwidth and the amount of underutilized spectrum, DSA (Dynamic Spectrum Access) networks employing cognitive radios are a solution that can revolutionize the telecommunications industry, significantly changing the way we use spectrum resources, and design wireless systems and services. Cognitive radio has improve the spectral efficiency of licensed radio frequency bands by accessing unused part of the band opportunistically without interfering with a license primary user PU. In this thesis we investigate the effects on the quality of service (QoS) performance of spectrum management techniques for the connection-based channel usage behavior for Secondary user (SU). This study also consider other factors such as spectrum sensing time, spectrum handoff and generally distributed service time and channel contention for different SUs. The preemptive resume priority M/G/1 queuing theory is used to characterize the above mentioned effects. The proposed structure of the model can integrate various system parameters such spectrum sensing, spectrum decision, spectrum sharing and spectrum handoff.<br>Sajid Mahmood 0046-762788990 Mujeeb Abdullah 0046-760908069
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SUN, YUHANG. "Spectrum Sensing in Cognitive Radio Systems using Energy Detection :." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-10789.
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Cognitive radio is a low-cost communication system, which can choose the available frequencies and waveforms automatically on the premise of avoiding interfering the licensed users. The spectrum sensing is the key enabling technology in cognitive radio networks. It is able to fill voids in the wireless spectrum and can dramatically increase spectral efficiency. In this thesis, the author use matlab to simulate the received signals from the cognitive radio networks and an energy detector to detect whether the spectrum is being used. The report also compares the theoretical value and the simulated result and then describes the relationship between the signal to noise ratio (SNR) and the detections. At last, the method, energy detection and simulation and result are discussed which is considered as the guidelines for the future work.
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Moghimi, Farzad. "Spectrum sensing and throughput maximization in cognitive radio networks." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42653.
Full textAbstract:
In cognitive radio (CR) systems, reliable spectrum sensing techniques are required in order to avoid interference to the primary users (PUs) of the spectrum. In this dissertation two spectrum sensing techniques are developed, and sensing time and power allocation are optimized in multi-input multi-output (MIMO) CR systems.
The motivation of the first proposed spectrum sensing technique is that, in practice, CRs also have to cope with various types of non-Gaussian noise such as man-made impulsive noise, and co-channel interference. However, most of the existing literature on spectrum sensing only considers impairment by additive white Gaussian noise (AWGN). To address this issue, we propose an Lp-norm detector which has tunable parameters that can be adjusted for the underlying type of noise. We also propose an adaptive algorithm for optimization of the Lp-norm parameters which does not require any a priori knowledge of the noise statistics. The motivation for the second proposed spectrum sensing technique is that the signals transmitted by PUs often also contain known pilot symbols for synchronization and channel estimation purposes. Coherent correlation based spectrum sensing techniques can exploit these known symbols but waste the energy contained in the data symbols. Hence, while considering AWGN impairment, we propose a hybrid coherent/energy detection scheme which exploits both the pilot and the data symbols transmitted by the PU. Since the complexity of the globally optimal hybrid detection metric is very high, we develop a simple locally optimal hybrid metric, which turns out to be a linear combination of an energy detection metric and a correlation metric.
While the proposed methods improve the accuracy of spectrum sensing, there exists a tradeoff between sensing time and transmission time for CR networks. In this thesis, we investigate this issue for conventional energy detection in MIMO CR networks. Specifically, we optimize the sensing threshold, sensing time, and transmit power of both single-band and multi-band MIMO CR systems for maximization of the opportunistic throughput under transmit power, probability of false alarm, and probability of detection constraints. We also develop efficient iterative algorithms for solving these non-convex optimization problems based on the concept of alternating optimization.
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Radhi, Nazar Mortada. "Implementation of spectrum sensing techniques for cognitive radio systems." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/7381.
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This work presents a method for real-time detection of secondary users at the cognitive wireless technologies base stations. Cognitive radios may hide themselves in between the primary users to avoid being charged for spectrum usage. To deal with such scenarios, a cyclostationary Fast Fourier Transform accumulation method (FAM) has been used to develop a new strategy for recognising channel users under perfect and different noise environment conditions. Channel users are tracked according to the changes in their signal parameters, such as modulation techniques. MATLAB® Simulation tool was used to run various modulation signals on channels, and the obtained spectral correlation density function shows successful recognition between secondary and primary signals. We are unaware of previous efforts to use the FAM characteristics or other detection methods to make a distinction between channel users as presented in this thesis. A novel combination of both cognitive radio technology and ultra wideband technology is interdicted in this thesis, looking for an efficient and reliable spectrum sensing method to detect the presence of primary transmitters, and a number of spectrum-sensing techniques implemented in ultra wideband and cognitive radio component (UWB-CR) under different AWGN and fading settings environments. The sensing performance of different detectors is compared in conditions of probability of detection and miss detection curves. Simulation results show that the selection of detectors rely on the different fading scenarios, detector requirements and on a priori knowledge. Furthermore, result showed that the matched filter detection method is suitable for detecting signals through UWB-CR system under various fading channels. A general observation is that the matched filter detector outperforms the other detectors in all scenarios by an average of SNR=-20 dB in the level of probability of detection (Pd) , and the energy detector slightly outperforms the cyclostationary detector, in the level Pd at SNR=-20 dB. Furthermore, the thesis adapts novel detection models of cooperative and cluster cooperative wideband spectrum sensing in cognitive radio networks. In the proposed schemes, wavelet-based multi-resolution spectrum sensing and a proposed approach scheme are utilized for improving sensing performance of both models. On the other hand, cluster based cooperative spectrum sensing with soft combination Equal Gain Combination (EGC) scheme is proposed. The proposed detection models could achieve improvement of transmitter signal detection in terms of higher probability of detection and lower probability of false alarm. In the cooperative wideband spectrum sensing model, using traditional fusion rule, existing worst performance of false alarms by measurement is 78% of the sensing bands at an average SNR=5 dB; this compares with the proposed model, which is by measurement 19% false alarms of scanning spectrum at the same SNR for cluster cooperative wideband spectrum sensing. The proposed combining methods shows improvements of results with a high probability of detection (Pd) and low probability of false alarm (Pf) at an average SNR=-16 dB compared with other traditional fusion methods; this is illustrated through numerical results.
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Chen, Changlong. "Robust and Secure Spectrum Sensing in Cognitive Radio Networks." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1383316543.
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Kozal, Ahmed Sultan Bilal. "Multi user cooperation spectrum sensing in wireless cognitive radio networks." Thesis, Liverpool John Moores University, 2015. http://researchonline.ljmu.ac.uk/4474/.
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With the rapid proliferation of new wireless communication devices and services, the demand for the radio spectrum is increasing at a rapid rate, which leads to making the spectrum more and more crowded. The limited available spectrum and the inefficiency in the spectrum usage have led to the emergence of cognitive radio (CR) and dynamic spectrum access (DSA) technologies, which enable future wireless communication systems to exploit the empty spectrum in an opportunistic manner. To do so, future wireless devices should be aware of their surrounding radio environment in order to adapt their operating parameters according to the real-time conditions of the radio environment. From this viewpoint, spectrum sensing is becoming increasingly important to new and future wireless communication systems, which is designed to monitor the usage of the radio spectrum and reliably identify the unused bands to enable wireless devices to switch from one vacant band to another, thereby achieving flexible, reliable, and efficient spectrum utilisation. This thesis focuses on issues related to local and cooperative spectrum sensing for CR networks, which need to be resolved. These include the problems of noise uncertainty and detection in low signal to noise ratio (SNR) environments in individual spectrum sensing. In addition to issues of energy consumption, sensing delay and reporting error in cooperative spectrum sensing. In this thesis, we investigate how to improve spectrum sensing algorithms to increase their detection performance and achieving energy efficiency. To this end, first, we propose a new spectrum sensing algorithm based on energy detection that increases the reliability of individual spectrum sensing. In spite of the fact that the energy detection is still the most common detection mechanism for spectrum sensing due to its simplicity. Energy detection does not require any prior knowledge of primary signals, but has the drawbacks of threshold selection, and poor performance due to noise uncertainty especially at low SNR. Therefore, a new adaptive optimal energy detection algorithm (AOED) is presented in this thesis. In comparison with the existing energy detection schemes the detection performance achieved through AOED algorithm is higher. Secondly, as cooperative spectrum sensing (CSS) can give further improvement in the detection reliability, the AOED algorithm is extended to cooperative sensing; in which multiple cognitive users collaborate to detect the primary transmission. The new combined approach (AOED and CSS) is shown to be more reliable detection than the individual detection scheme, where the hidden terminal problem can be mitigated. Furthermore, an optimal fusion strategy for hard-fusion based cognitive radio networks is presented, which optimises sensing performance. Thirdly, the need for denser deployment of base stations to satisfy the estimated high traffic demand in future wireless networks leads to a significant increase in energy consumption. Moreover, in large-scale cognitive radio networks some of cooperative devices may be located far away from the fusion centre, which causes an increase in the error rate of reporting channel, and thus deteriorating the performance of cooperative spectrum sensing. To overcome these problems, a new multi-hop cluster based cooperative spectrum sensing (MHCCSS) scheme is proposed, where only cluster heads are allowed to send their cluster results to the fusion centre via successive cluster heads, based on higher SNR of communication channel between cluster heads. Furthermore, in decentralised CSS as in cognitive radio Ad Hoc networks (CRAHNs), where there is no fusion centre, each cognitive user performs the local spectrum sensing and shares the sensing information with its neighbours and then makes its decision on the spectrum availability based on its own sensing information and the neighbours’ information. However, cooperation between cognitive users consumes significant energy due to heavy communications. In addition to this, each CR user has asynchronous sensing and transmission schedules which add new challenges in implementing CSS in CRAHNs. In this thesis, a new multi-hop cluster based CSS scheme has been proposed for CRAHNs, which can enhance the cooperative sensing performance and reduce the energy consumption compared with other conventional decentralised cooperative spectrum sensing modes.
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Ma, Xiao. "Spectrum sensing based on sequential testing." Thesis, University of Canterbury. Electrical and Computer Engineering, 2010. http://hdl.handle.net/10092/3682.
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Recently, interest has been shown in cognitive radio (CR) systems since they can op-
portunistically access unused spectrum bands thereby increasing usable communication
capacity. Spectrum sensing has been identified as a key function to ensure that CR can
detect spectrum holes. In a CR network, a fast and accurate spectrum sensing scheme is
important.
Spectrum sensing can be viewed as a signal detection problem. Most of the existing
spectrum sensing schemes are based on fixed sample size detectors which means that their
sensing time is preset and fixed. However, the work of Wald [27] showed that a detector
based on sequential detection requires less average sensing time than a fixed sample size
detector.
In this thesis, we have applied the method of sequential detection to reduce the average
sensing time. Simulation results have shown that, compared to the fixed sample size
energy detector, a sequential detector can reduce sensing time by up to 85% in the AWGN
channel for the same detection performance. In order to limit sensing time, especially
in a fading environment, a truncated sequential detector is developed. The simulation
results show that the truncated sequential detector requires less sensing time than the
sequential detector, but the performance degrades due to truncation. Finally, a cooperative
spectrum sensing scheme is used where each individual sensor uses a sequential detector.
The combining rule used at the fusion center is a selection combining rule. Simulation
results show that the proposed cooperative spectrum sensing scheme can reduce the sensing
time compared to the individual spectrum sensing scheme.
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Malkireddy, Sivakesava Reddy. "Spectrum Sensing of acoustic OFDM signals." Thesis, Linköpings universitet, Kommunikationssystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-86811.
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OFDM is a fast growing technology in the area of wireless communication due to its numerous advantages and applications. The current and future technologies in the area of wireless communications like WiMAX, WiFi, LTE, MBWA and DVB-T uses the OFDM signals. The OFDM technology is applicable to the radio communication as well as the acoustic communication. Though the licensed spectrum is intended to be used only by the spectrum owners, Cognitive radio is a concept of reusing this licensed spectrum in an unlicensed manner. Cognitive radio is motivated by the measurements of spectrum utilization . Cognitive radio must be able to detect very weak primary users signal and to keep the interference level at a maximum acceptable level. Hence spectrum sensing is an essential part of the cognitive radio. Spectrum is a scarce resource and spectrum sensing is the process of identifying the unused spectrum, without causing any harm to the existing primary user’s signal. The unused spectrum is referred to as spectrum hole or white space and this spectrum hole could be reused by the cognitive radio. This thesis work focuses on implementing primary acoustic transmitter to transmit the OFDM signals from a computer through loudspeaker and receive the signals through a microphone. Then by applying different detection methods on the received OFDM signal for detection of the spectrum hole, the performance of these detection methods is compared here. The commonly used detection methods are power spectrum estimation, energy detection and second–order statistics (GLRT approach, Autocorrelation Function (ACF) detection and cyclostationary feature detection ). The detector based on GLRT approach exploits the structure of the OFDM signal by using the second order statistics of the received data. The thesis mainly focuses on GLRT approach and ACF detectors and compare their performance.
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Wang, Geng. "Performance of collaborative spectrum sensing in a cognitive radio system." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/14834.
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Cognitive radio (CR) is a novel approach to improving the spectral efficiency of
licensed radio frequency bands by opportunistically accessing unused portions of the band without introducing undue interference to a licensed user. To reliably identify unused portions in a dynamic environment, a collaborative spectrum sensing (CSS) approach is known to be advantageous. In this thesis, we investigate two issues related to CSS. A weighted energy fusion scheme for secondary users (SUs) with different sensing channel conditions is shown to achieve good sensing performance. To analyze the performance, a numerical approach utilizing a result in the probability density function of the weighted sum of noncentral chi-square random variables is used. Simulation
results confirm the viability of the proposed numerical approach. The performance degradation resulting from imperfect reporting channels and energy measurement quantization in a CSS system is investigated. Simulation results show that the sensing performance can be significantly degraded. To reduce the performance degradation, unequal error protection of transmitted symbols through unequal power allocation (UPA) is employed. Simulation results are provided to quantify the gain provided by UPA.
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Mohamad, Usama Yusuf [Verfasser]. "Space-Time Spectrum Sensing for Cognitive Radio / Usama Yusuf Mohamad." Kassel : Kassel University Press, 2019. http://d-nb.info/120689962X/34.
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Wang, Nan. "Threshold setting algorithms for spectrum sensing in cognitive radio networks." Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/9064.
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As the demand for wireless communication services grows quickly, spectrum scarcity has been on the rise sharply. In this context, cognitive radio (CR) is being viewed as a new intelligent technology to solve the deficiency of fixed spectrum assignment policy in wireless communications. Spectrum sensing is one of the most fundamental technologies to realise dynamic spectrum access in cognitive radio networks. It requires high accuracy as well as low complexity. In this thesis, a novel adaptive threshold setting algorithm is proposed to optimise the trade-off between detection and false alarm probability in spectrum sensing while satisfying sensing targets set by the IEEE 802.22 standard. The adaptive threshold setting algorithm is further applied to minimise the error decision probability with varying primary users' spectrum utilisations. A closed-form expression for the error decision probability, satisfied SNR value, number of samples and primary users' spectrum utilisation ratio are derived in both fixed and the proposed adaptive threshold setting algorithms. By implementing both Welch and wavelet based energy detectors, the adaptive threshold setting algorithm demonstrates a more reliable and robust sensing result for both primary users (PUs) and secondary users (SUs) in comparison with the conventional fixed one. Furthermore, the wavelet de-noising method is applied to improve the sensing performance when there is insu cient number of samples. Finally, a novel database assisted spectrum sensing algorithm is proposed for a secondary access of the TV White Space (TVWS) spectrum. The proposed database assisted sensing algorithm is based on the developed database assisted approach for detecting incumbents like Digital Terrestrial Television (DTT) and Programme Making and Special Events (PMSE), but assisted by spectrum sensing to further improve the protection to primary users. Monte-Carlo simulations show a higher SUs' spectrum efficiency can be obtained for the proposed database assisted sensing algorithm than the existing stand-alone database assisted or sensing models.
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Gismalla, Yousif Ebtihal. "Performance analysis of spectrum sensing techniques for cognitive radio systems." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/performance-analysis-of-spectrum-sensing-techniques-for-cognitive-radio-systems(157fe1af-717c-4705-a649-d809766cf5cb).html.
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Cognitive radio is a technology that aims to maximize the current usage of the licensed frequency spectrum. Cognitive radio aims to provide services for license-exempt users by making use of dynamic spectrum access (DSA) and opportunistic spectrum sharing strategies (OSS). Cognitive radios are defined as intelligent wireless devices capable of adapting their communication parameters in order to operate within underutilized bands while avoiding causing interference to licensed users. An underused band of frequencies in a specific location or time is known as a spectrum hole. Therefore, in order to locate spectrum holes, reliable spectrum sensing algorithms are crucial to facilitate the evolution of cognitive radio networks. Since a large and growing body of literature has mainly focused into the conventional time domain (TD) energy detector, throughout this thesis the problem of spectrum sensing is investigated within the context of a frequency domain (FD) approach. The purpose of this study is to investigate detection based on methods of nonparametric power spectrum estimation. The considered methods are the periodogram, Bartlett's method, Welch overlapped segments averaging (WOSA) and the Multitaper estimator (MTE). Another major motivation is that the MTE is strongly recommended for the application of cognitive radios. This study aims to derive the detector performance measures for each case. Another aim is to investigate and highlight the main differences between the TD and the FD approaches. The performance is addressed for independent and identically distributed (i.i.d.) Rayleigh channels and the general Rician and Nakagami fading channels. For each of the investigated detectors, the analytical models are obtained by studying the characteristics of the Hermitian quadratic form representation of the decision statistic and the matrix of the Hermitian form is identified. The results of the study have revealed the high accuracy of the derived mathematical models. Moreover, it is found that the TD detector differs from the FD detector in a number of aspects. One principal and generalized conclusion is that all the investigated FD methods provide a reduced probability of false alarm when compared with the TD detector. Also, for the case of periodogram, the probability of sensing errors is independent of the length of observations, whereas in time domain the probability of false alarm is increased when the sample size increases. The probability of false alarm is further reduced when diversity reception is employed. Furthermore, compared to the periodogram, both Bartlett method and Welch method provide better performance in terms of lower probability of false alarm but an increased probability of detection for a given probability of false alarm. Also, the performance of both Bartlett's method and WOSA is sensitive to the number of segments, whereas WOSA is also sensitive to the overlapping factor. Finally, the performance of the MTE is dependent on the number of employed discrete prolate spheroidal (Slepian) sequences, and the MTE outperforms the periodogram, Bartlett's method and WOSA, as it provides the minimal probability of false alarm.
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Suratman, Fiky Y. "Spectrum Sensing in Cognitive Radio: Bootstrap and Sequential Detection Approaches." Phd thesis, Fachgebiet Signalverarbeitung, 2014. http://tuprints.ulb.tu-darmstadt.de/3808/1/DoctoralDissertation_Fiky_v1.pdf.
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In this thesis, advanced techniques for spectrum sensing in cognitive radio are addressed. The problem of small sample size in spectrum sensing is considered, and resampling-based methods are developed for local and collaborative spectrum sensing. A method to deal with unknown parameters in sequential testing for spectrum sensing is proposed. Moreover, techniques are developed for multiband sensing, spectrum sensing in low signal to noise ratio, and two-bits hard decision combining for collaborative spectrum sensing.
The assumption of using large sample size in spectrum sensing often raises a problem when the devised test statistic is implemented with a small sample size. This is because, for small sample sizes, the asymptotic approximation for the distribution of the test statistic under the null hypothesis fails to model the true distribution.
Therefore, the probability of false alarm or miss detection of the test statistic is poor. In this respect, we propose to use bootstrap methods, where the distribution of the test statistic is estimated by resampling the observed data. For local spectrum sensing, we propose the null-resampling bootstrap test which exhibits better performances than the pivot bootstrap test and the asymptotic test, as common approaches. For collaborative spectrum sensing, a resampling-based Chair-Varshney fusion rule is developed. At the cognitive radio user,
a combination of independent resampling and moving-block resampling is proposed to estimate the local probability of detection. At the fusion center, the parametric bootstrap is applied
when the number of cognitive radio users is large.
The sequential probability ratio test (SPRT) is designed to test a simple hypothesis against a simple alternative hypothesis.
However, the more realistic scenario in spectrum sensing is to deal with composite hypotheses, where the parameters are not uniquely defined. In this thesis, we generalize the sequential probability ratio test to cope with composite hypotheses, wherein the thresholds are updated in an adaptive manner, using the parametric bootstrap.
The resulting test avoids the asymptotic assumption made in earlier works. The proposed bootstrap based sequential probability ratio test minimizes decision errors due to errors induced by employing maximum likelihood estimators in the generalized sequential probability ratio test. Hence, the proposed method achieves the sensing objective. The average sample number (ASN) of the proposed method is better than that of the conventional method which uses the asymptotic assumption. Furthermore, we propose a mechanism to reduce the computational cost incurred by the bootstrap, using a convex combination of the latest K bootstrap distributions. The reduction in the computational cost does not impose a significant increase on the ASN, while the protection against decision errors is even better. This work is motivated by the fact that the sequential probability ratio test produces a smaller sensing time than its counterpart of fixed sample size test. A smaller sensing time is preferable to improve the throughput of the cognitive radio network.
Moreover, multiband spectrum sensing is addressed, more precisely by using multiple testing procedures. In a context of a fixed sample size, an adaptive Benjamini-Hochberg procedure is suggested to be used, since it produces a better balance between the familywise error rate and the familywise miss detection, than the conventional Benjamini-Hochberg. For the sequential probability ratio test, we devise a method based on ordered stopping times. The results show that our method has smaller ASNs than the Bonferroni procedure.
Another issue in spectrum sensing is to detect a signal when the signal to noise ratio is very low. In this case, we derive a locally optimum detector that is based on the assumption that the underlying noise is Student's t-distributed. The resulting scheme outperforms the energy detector in all scenarios. Last but not least, we extend the hard decision combining in collaborative spectrum sensing to include a quality information bit. In this case, the multiple thresholds are determined by a distance measure criterion. The hard decision combining with quality information performs better than the conventional hard decision combining.
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Stegman, Jason Karl. "Wideband and Narrowband Spectrum Sensing Methods Using Software Defined Radios." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/theses/1469.
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The ability to accurately sense the surrounding wireless spectrum, without having any prior information about the type of signals present, is an important aspect for dynamic spectrum access and cognitive radio. Energy detection is one viable method, however its performance is limited at low SNR and must adhere to Nyquist sampling theorem. Compressive sensing has emerged as a potential method to recover wideband signals using sub-Nyquist sampling rates, under the presumption that the signals are sparse in a certain domain. In this study, the performance and some of the practical limitations of energy detection and compressive sensing are compared via simulation, and also implementation using the Universal Software Radio Peripheral (USRP) software defined radio (SDR) platform. The usefulness and simplicity of the USRP and GNU Radio software toolkit for simulation and experimentation, as well as some other application areas of compressive sensing and SDR, is also discussed.
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Mousavifar, Seyed Ali. "Trust-based spectrum and energy efficient collaborative spectrum sensing in cognitive radio networks." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/52761.
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Cognitive radio (CR) is a promising technology designed to improve the utilization of lightly used portions of the licensed spectrum while ensuring no undue interference with incumbent users (IUs). CR networks (CRNs) employ collaborative spectrum sensing (CSS) methods to discover spectrum opportunities. Spectrum and energy overhead costs play important roles in the efficiency of CSS in CRNs.
A trust-based energy efficient CSS (EE-CSS) protocol is proposed. The protocol achieves energy efficiency by reducing the total number of sensing reports exchanged between the secondary users (SUs) and the fusion center (FC) in the presence of misbehaving SUs (MSUs). The steady-state and transient behavior of the average number of sensing reports and trust values of SUs in EE-CSS are analyzed and compared to those in traditional CSS (T-CSS). The impact of link outages on the global false alarm (FA) probabilities, ℚf, and the global miss detection (MD) probabilities, ℚmd, in EE-CSS and T-CSS is also analyzed.
A centralized trust-based collusion attack strategy, in conjunction with integer linear programming, is proposed to compromise the decision of the FC in EE-CSS. The proposed strategy aims to attack only when it is likely to alter the decision of the FC. A mitigating scheme, based on the cross-correlation of sensing reports, is proposed to identify SUs with abnormal behaviors and to eliminate them from the decision making process at the FC.
We also propose a trust-based spectrum and energy efficient CSS (SEE-CSS) scheme for the IEEE 802.22 standard wireless regional area network (WRAN). The proposed scheme aims to reduce the number of urgent coexistence situation (UCS) notifications transmitted from customer premise equipment (CPE) nodes to the WRAN base station (BS). The UCS messages inform the BS of the presence of active IUs on the licensed spectrum. We adapt the collusion attack strategy for SEE-CSS and apply the cross-correlation method at the BS to mitigate against the collusion attack. The results show that while ℚf and ℚmd are kept the same in T-CSS and SEE-CSS, the SEE-CSS protocol is more energy and spectrum efficient.<br>Applied Science, Faculty of<br>Electrical and Computer Engineering, Department of<br>Graduate
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Park, Jongmin. "CMOS analog spectrum processing techniques for cognitive radio applications." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/37230.
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The objective of the research is to develop analog spectrum processing techniques for cognitive radio (CR) applications in CMOS technology. CR systems aim to use the unoccupied spectrum allocations without any license when the primary users are not present. Therefore, the successful deployment of CR systems relies on their ability to accurately sense the spectrum usage status over a wide frequency range serving various wireless communication standards. Meanwhile, to maximize the utilization of the available spectrum segments, the bandwidth of the signal has to be highly flexible, so that even a small fraction of spectrum resources can be fully utilized by CR users. One of the key enabling technologies of variable bandwidth communication is a tunable baseband filter.
In this research, a reconfigurable CR testbed system is presented as groundwork for the researches related with CR systems. With the feasibility study on the multi-resolution spectrum sensing (MRSS) functionality, a method for determining sensing threshold for MRSS functionality is presented, and a fully integrated MRSS receiver in CMOS technology is demonstrated. On the other hand, a reconfigurable CMOS analog baseband filter which can change its bandwidth, type and order with high resolution for CR applications is presented. In sum, an analog spectrum sensing method as well as a highly flexible analog baseband filter architecture is established and implemented in CMOS technology. Both designs are targeting the utilization of the analog signal processing capability with the aid of the digital circuits.
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Lee, Won Yeol. "Spectrum management in cognitive radio wireless networks." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31712.
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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Akyildiz, Ian; Committee Member: Ammar, Mostafa; Committee Member: Laskar, Joy; Committee Member: Li, Ye; Committee Member: Sivakumar, Raghupathy. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Panahandeh, Ali. "Multi-polarized sensing for cognitive radio." Doctoral thesis, Universite Libre de Bruxelles, 2012. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209586.
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In this thesis the multi-polarized Cognitive Radios are studied. Cognitive Radios are proposed as an interesting way to more efficiently use the frequency resources. A Cognitive Radio secondary user finds the frequency bands which are not utilized by primary users and communicates on them without interfering with the primary users. In order to achieve this goal the secondary user must be able to detect reliably and quickly the presence of a primary user in a frequency band. In this thesis, the impact of polarization on the spectrum sensing performances of cognitive radio systems is studied.<p><p>First the depolarization occurring in the wireless channel is studied for two cognitive radio scenarios. This is done through an extensive measurement campaign in two outdoor-to-indoor and indoor-to-indoor scenarios where the parameters characterizing the radiowaves polarization are characterized at three different spatial scales: small-scale variation, large-scale variation and distance variation. <p><p>Second, a new approach is proposed in modeling of multi-polarized channels. The polarization of received fields is characterized from an electromagnetic point of view by modeling the polarization ellipse. Theoretical formulations are proposed in order to obtain the parameters characterizing the polarization ellipse based on the signals received on three cross-polarized antennas. A system-based statistical model of the time-dynamics of polarization is proposed based on an indoor-to-indoor measurement campaign. The analytical formulations needed in order to project the polarization ellipse onto a polarized multi-antenna system are given and it is shown how the model can be generated. <p><p>Third, the impact of polarization on the spectrum sensing performances of energy detection method is presented and its importance is highlighted. The performance of spectrum sensing with multi-polarized antennas is compared with unipolar single and multi-antenna systems. This analysis is based on an analytical formulation applied to the results obtained from the multi-polarized measurement campaign. The detection probability as a function of distance between the primary transmitter and the secondary terminal and the inter-antenna correlation effect on the spectrum sensing performance are studied. <p><p>An important limitation of energy detector is its dependence on the knowledge of the noise variance. An uncertainty on the estimation of the noise variance considerably affects the performance of energy detector. This limitation is resolved by proposing new multi-polarized spectrum sensing methods which do not require any knowledge neither on the primary signal nor on the noise variance. These methods, referred to as “Blind spectrum sensing methods”, are based on the use of three cross-polarized antennas at the secondary terminal. Based on an analytical formulation and the results obtained from the measurement campaign, the performances of the proposed methods are compared with each-other and with the energy detection method. The effect of antenna orientation on the spectrum sensing performance of the proposed methods and the energy detection method is studied using the proposed elliptical polarization model. <p><br>Doctorat en Sciences de l'ingénieur<br>info:eu-repo/semantics/nonPublished
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Gottapu, Srinivasa Kiran. "Deep Learning Approach for Sensing Cognitive Radio Channel Status." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1609087/.
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Cognitive Radio (CR) technology creates the opportunity for unlicensed users to make use of the spectral band provided it does not interfere with any licensed user. It is a prominent tool with spectrum sensing functionality to identify idle channels and let the unlicensed users avail them. Thus, the CR technology provides the consumers access to a very large spectrum, quality spectral utilization, and energy efficiency due to spectral load balancing. However, the full potential of the CR technology can be realized only with CRs equipped with accurate mechanisms to predict/sense the spectral holes and vacant spectral bands without any prior knowledge about the characteristics of traffic in a real-time environment. Multi-layered perception (MLP), the popular neural network trained with the back-propagation (BP) learning algorithm, is a keen tool for classification of the spectral bands into "busy" or "idle" states without any a priori knowledge about the user system features. In this dissertation, we proposed the use of an evolutionary algorithm, Bacterial Foraging Optimization Algorithm (BFOA), for the training of the MLP NN. We have compared the performance of the proposed system with the traditional algorithm and with the Hybrid GA-PSO method. With the results of a simulation experiment that this new learning algorithm for prediction of channel states outperforms the traditional BP algorithm and Hybrid GA-PSO method with respect to classification accuracy, probability of misdetection, and Probability of false alarm.
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Gonzales, Fuentes Lee. "HELPING COGNITIVE RADIO IN THE SEARCH FOR FREE SPACE." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-11495.
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Spectrum sensing is an essential pre-processing step of cognitive radio technology for dynamic radio spectrum management. One of the main functions of Cognitive radios is to detect the unused spectrum and share it without harmful interference with other users. The detection of signal components present within a determined frequency band is an important requirement of any sensing technique. Most methods are restricted to the detection of the spectral lines. However, these methods may not comply with the needs imposed by practical applications. This master thesis work presents a novel method to detect significant spectral components in measured non-flat spectra by classifying them in two groups: signal and noise frequency lines. The algorithm based on Fisher’s discriminant analysis, aside from the detection of spectral lines, estimates the magnitude of the spectral lines and provides a measure of the quality of classification to determine if a spectral line was incorrectly classified. Furthermore, the frequency lines with higher probability of misclassification are regrouped and the validation process recomputed, which results in lower probabilities of misclassification. The proposed automatic detection algorithm requires no user interaction since any prior knowledge about the measured signal and the noise power is needed. The presence or absence of a signal regardless of the shape of the spectrum can be detected. Hence, this method becomes a strong basis for high-quality operation mode of cognitive radios. Simulation and measurement results prove the advantages of the presented technique. The performance of the technique is evaluated for different signal-to-noise ratios (SNR) ranging from 0 to -21dB as required by the IEEE standard for smart radios. The method is compared with previous signal detection methods.
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Gong, Xitao [Verfasser]. "Spectrum sensing and interference mitigation in cognitive radio networks / Xitao Gong." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2014. http://d-nb.info/1052160514/34.
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Bollig, Andreas Verfasser], Rudolf [Akademischer Betreuer] [Mathar, and Reiner [Akademischer Betreuer] Thomä. "Spectrum sensing in cognitive radio / Andreas Bollig ; Rudolf Mathar, Reiner Thomä." Aachen : Universitätsbibliothek der RWTH Aachen, 2016. http://d-nb.info/113079248X/34.
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Cao, Hanwen [Verfasser]. "Towards versatile and robust spectrum sensing in cognitive radio / Hanwen Cao." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2012. http://d-nb.info/1029474478/34.
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Sudhamani, Chilakala [Verfasser]. "Performance of Cooperative Spectrum Sensing in Cognitive Radio Networks / Chilakala Sudhamani." München : GRIN Verlag, 2020. http://d-nb.info/1219301957/34.
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Nasser, Abbass. "Spectrum sensing for half and full-duplex interweave cognitive radio systems." Thesis, Brest, 2017. http://www.theses.fr/2017BRES0006/document.
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En raison de la demande croissante de services de communication sans fil et de la limitation des ressources de spectre, la radio cognitive (CR) a été initialement proposée pour résoudre la pénurie de spectre. CR divise les systèmes transmetteurs-récepteurs de communication en deux catégories : les Utilisateurs Principaux (PU) et les Utilisateurs Secondaires (SU). PU a le droit légal d'utiliser la bande spectrale, tandis que SU est un utilisateur opportuniste qui peut transmettre sur cette bande chaque fois qu'elle est vacante afin d'éviter toute interférence avec le signal de PU. De ce fait, la détection des activités de PU devient une priorité principale pour toute CR.Le Spectrum Sensing devient ainsi une partie importante d’un système CR, qui surveille les transmissions de PU. En effet, le Spectrum Sensing joue un rôle essentiel dans le mécanisme du fonctionnement du CR en localisant les canaux disponibles et, d'autre part, en protégeant les canaux occupés des interférences de la transmission SU. En fait, Spectrum Sensing a gagné beaucoup d'attention au cours de la dernière décennie, et de nombreux algorithmes sont proposés. Concernant la fiabilité de la performance, plusieurs défis comme le faible rapport signal sur bruit, l'incertitude de bruit (NU), la durée de détection du spectre, etc. Cette thèse aborde les défis de la détection du spectre et apporte quelques solutions. De nouveaux détecteurs basés sur la détection des caractéristiques cyclo-stationnaires et la densité spectrale de puissance (PSD) du signal de PU sont présentés. Un algorithme de test de signification de corrélation canonique (CCST) est proposé pour effectuer une détection cyclo-stationnaire. CCST peut détecter la présence des caractéristiques cycliques communes parmi les versions retardées du signal reçu. Ce test peut révéler la présence d'un signal cyclo-stationnaire dans le signal de mélange reçu. Une autre méthode de détection basée sur la PSD cumulative est proposée. En supposant que le bruit est blanc (sa PSD est plate), la PSD cumulative s'approche d'une droite. Cette forme devient non linéaire pour les signaux de télécommunication. Distinguer la forme cumulative PSD peut donc conduire à diagnostiquer l'état du canal.La radio cognitive Full-Duplex (FD-CR) a également été étudiée dans ce manuscrit, où plusieurs défis sont analysés en proposant de nouvelles contributions. Le fonctionnement FD permet au CR d'éviter la période de silence pendant la détection du spectre. Dans le système CR classique, le SU cesse de transmettre pendant la détection du spectre afin de ne pas affecter la fiabilité de détection. Dans FD-CR, SU peut éliminer la réflexion de son signal transmis et en même temps réaliser le Spectrum Sensing. En raison de certaines limitations, le résidu de l'auto-interférence ne peut pas être complètement annulé, alors la crédibilité de la détection du spectre est fortement affectée. Afin de réduire la puissance résiduelle, une nouvelle architecture de récepteur SU est élaborée pour atténuer les imperfections du circuit (comme le bruit de phase et la distorsion non linéaire de l'amplificateur à faible bruit du récepteur). La nouvelle architecture montre sa robustesse en assurant une détection fiable et en améliorant le débit de SU<br>Due to the increasing demand of wireless communication services and the limitation in the spectrum resources, Cognitive Radio (CR) has been initially proposed in order to solve the spectrum scarcity. CR divides the communication transceiver into two categories: the Primary (PU) or the Secondary (SU) Users. PU has the legal right to use the spectrum bandwidth, while SU is an opportunistic user that can transmit on that bandwidth whenever it is vacant in order to avoid any interference to the signal of PU. Hence the detection of PU becomes a main priority for CR systems. The Spectrum Sensing is the part of the CR system, which monitors the PU activities. Spectrum Sensing plays an essential role in the mechanism of the CR functioning. It provides CR with the available channel in order to access them, and on the other hand, it protects occupied channels from the interference of the SU transmission. In fact, Spectrum Sensing has gained a lot of attention in the last decade, and numerous algorithms are proposed to perform it. Concerning the reliability of the performance, several challenges have been addressed, such as the low Signal to Noise Ratio (SNR), the Noise Uncertainty (NU), the Spectrum Sensing duration, etc. This dissertation addresses the Spectrum Sensing challenges and some solutions are proposed. New detectors based on Cyclo-Stationary Features detection and the Power Spectral Density (PSD) of the PU are presented. CanonicalCorrelation Significance Test (CCST) algorithm is proposed to perform cyclo-stationary detection. CCST can detect the presence of the common cyclic features among the delayed versions of the received signal. This test can reveal the presence of a cyclo-stationary signal in the received mixture signal. Another detection method based on the cumulative PSD is proposed. By assuming the whiteness of the noise (its PSD is at), the cumulative PSD approaches a straight line. This shape becomes non-linear when a telecommunication signal is present in the received mixture. Distinguishing the Cumulative PSD shape may lead to diagnose the channel status.Full-Duplex Cognitive Radio (FD-CR) has been also studied in this manuscript, where several challenges are analyzed by proposing a new contribution. FD functioning permits CR to avoid the silence period during the Spectrum Sensing. In classical CR system, SU stops transmitting during the Spectrum Sensing in order to do not affect the detection reliability. In FD-CR, SU can eliminate the reflection of its transmitted signal and at the same time achieving the Spectrum Sensing. Due to some limitations, the residual of the Self Interference cannot be completely cancelled, then the Spectrum Sensing credibility is highly affected. In order to reduce the residual power, a new SU receiver architecture is worked out to mitigate the hardware imperfections (such as the Phase Noise and the Non-Linear Distortion of the receiver Low-Noise Amplifier). The new architecture shows its robustness by ensuring a reliable detection and enhancing the throughput of SU
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Bollig, Andreas [Verfasser], Rudolf [Akademischer Betreuer] Mathar, and Reiner [Akademischer Betreuer] Thomä. "Spectrum sensing in cognitive radio / Andreas Bollig ; Rudolf Mathar, Reiner Thomä." Aachen : Universitätsbibliothek der RWTH Aachen, 2016. http://nbn-resolving.de/urn:nbn:de:hbz:82-rwth-2016-109259.
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Hamid, Mohamed. "On Finding Spectrum Opportunities in Cognitive Radios : Spectrum Sensing and Geo-locations Database." Licentiate thesis, KTH, Kommunikationssystem, CoS, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-110107.
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The spectacular growth in wireless services imposes scarcity in term of the available radio spectrum. A solution to overcome this scarcity is to adopt what so called cognitive radio based on dynamic spectrum access. With dynamic spectrum access, secondary (unlicensed) users can access spectrum owned by primary (licensed) users when it is temporally and/or geographically unused. This unused spectrum is termed as spectrum opportunity. Finding these spectrum opportunities related aspects are studied in this thesis where two approaches of finding spectrum opportunities, namely spectrum sensing and geo-locations databases are considered. In spectrum sensing arena, two topics are covered, blind spectrum sensing and sensing time and periodic sensing interval optimization. For blind spectrum sensing, a spectrum scanner based on maximum minimum eigenvalues detector and frequency domain rectangular filtering is developed. The measurements show that the proposed scanner outperforms the energy detector scanner in terms of the probability of detection. Continuing in blind spectrum sensing, a novel blind spectrum sensing technique based on discriminant analysis called spectrum discriminator has been developed in this thesis. Spectrum discriminator has been further developed to peel off multiple primary users with different transmission power from a wideband sensed spectrum. The spectrum discriminator performance is measured and compared with the maximum minimum eigenvalues detector in terms of the probability of false alarm, the probability of detection and the sensing time. For sensing time and periodic sensing interval optimization, a new approach that aims at maximizing the probability of right detection, the transmission efficiency and the captured opportunities is proposed and simulated. The proposed approach optimizes the sensing time and the periodic sensing interval iteratively. Additionally, the periodic sensing intervals for multiple channels are optimized to achieve as low sensing overhead and unexplored opportunities as possible for a multi channels system. The thesis considers radar bands and TV broadcasting bands to adopt geo-locations databases for spectrum opportunities. For radar bands, the possibility of spectrum sharing with secondary users in L, S and C bands is investigated. The simulation results show that band sharing is possible with more spectrum opportunities offered by C band than S and L band which comes as the least one. For the TV broadcasting bands, the thesis treats the power assignment for secondary users operate in Gävle area, Sweden. Furthermore, the interference that the TV transmitter would cause to the secondary users is measured in different locations in the same area.<br><p>QC 20130114</p><br>QUASAR
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Jia, Peng. "Spectrum-sensing threshold designs for cognitive radios." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97121.
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This thesis presents a study of optimal threshold designs for energy-detection based spectrum sensing in a cognitive-radio network with a pair of Primary-User (PU) transmitter-receiver and a pair of Secondary-User (SU) transmitter-receiver uniformly distributed in their corresponding transmission and interference ranges. Both large-scale path-loss and small-scale fading are considered in the wireless channel model. At first, the optimization problems under three criteria: Risk Minimization, Cost Minimization, and Capacity Maximization are formulated for SU with narrowband sensing capability. Derived under Bayesian framework, Risk Minimization threshold aims to minimize the sum of the miss detection and false alarm probabilities while Cost Minimization threshold considers minimizing the losses in PU and SU link rates due to miss detection and false alarm. On the other hand, Capacity Maximization thresholds are derived to achieve the maximum weighted sum of the PU and SU link rates. The performance of the derived optimal sensing thresholds is investigated and compared in terms of the achieved PU, SU link rates, and the sum capacity of the network for different PU transmission activity factors. Advantages of knowledge of PU and SU locations are also evaluated. Illustrative results show that SU opportunistic capacity can be obtained at the costs of some degradation in the PU link rate. In the narrowband sensing case, the Bayesian-based Risk Minimization and Cost Minimization thresholds are found to be more PU-link protective (i.e., introduce less degradation in PU link rate, especially at high PU transmission activity factor) while Capacity Maximization threshold can achieve the highest sum capacity of the network. The optimal threshold designs for narrowband sensing are further examined to include the constraint on guaranteed minimum degradation in the achieved PU rate. Under this constraint, the advantage in protecting the PU link of the Bayesian-based thresholds is no longer useful, and Capacity Maximization threshold is the better choice as it offers higher SU link rate and sum capacity. Furthermore, the study of optimum sensing thresholds for three criteria and without and with constraint is extended to consider the case of SU with wideband sensing capability along with a spectrum access algorithm, aiming at reducing the miss detection probability. Results show that, compared to the narrowband sensing, for all three criteria, the wideband sensing approach offers much better SU and PU link rates over the whole range of PU transmission activity factor and the resulting sum rate increases monotonically with the number of subcarriers. It is also confirmed that, for wideband sensing, Capacity Maximization threshold again outperforms the Bayesian-based thresholds to meet a much more stringent constraint on guaranteed PU rates while providing better SU link rates and sum rates.<br>Ce mémoire présente une étude de la conception de seuils optimaux pour les méthodes de perception du spectre basées sur le test d'énergie dans un réseau de radio cognitive, dans lequel se trouve une paire émetteur-récepteur pour l'Utilisateur Primaire (PU) et une paire émetteur-récepteur pour l'Utilisateur Secondaire (SU) uniformément distribuées dans leurs plages de transmission et d'interférence respectives. L'affaiblissement de propagation et l'évanouissement à petite échelle sont tous deux considérés dans le modèle du canal sans fil. En premier lieu, les problèmes d'optimisation sont formulés pour le PU avec possibilité de perception du spectre en bande étroite selon trois critères: minimisation du risque, minimisation du coût et maximisation de capacité. Obtenus grâce à un cadre bayésien, le seuil de minimisation du risque cherche à minimiser la somme des probabilités de détection manquée et de fausse alerte tandis que le seuil de minimisation du coût considère les pertes de débit du PU et SU dues à la détection manquée et à la fausse alerte. En revanche, les seuils de maximisation de capacité sont dérivés pour atteindre la somme pondérée maximum de débit du PU et SU. La performance des seuils optimaux dérivés est enquêtée et comparée selon le débit atteint par le PU et SU, ainsi que la somme des débits du réseau pour différents facteurs d'activités de transmission pour le PU. Les avantages apportés par la connaissance de la localisation du SU sont aussi évalués. Les résultats illustratifs démontrent que la capacité opportuniste du SU peut être obtenue au détriment du débit du PU. Dans le cas d'écoute en bande étroite, les seuils bayésiens obtenus avec la minimisation du risque et la minimisation du coût protègent mieux le PU (i.e., introduisent moins de dégradation du débit du PU, en particulier lorsque le facteur d'activité du PU est élevé) tandis que les seuils de maximization de capacité peuvent atteindre la plus haute capacité combinée du réseau. La conception de seuils optimaux pour la perception du spectre en bande étroite est examinée d'avantage pour inclure la contrainte qui garantit la dégradation minimale du débit atteignable du PU. Avec cette contrainte, il n'est plus avantageux de protéger le lien du PU des seuils bayésiens, et le seuil de maximisation de capacité représente un meilleur choix puisqu'il offre un plus haut débit pour le SU ainsi qu'une capacité combinée augmentée. De plus, l'étude des seuils optimaux pour les trois critères, avec et sans contrainte, est étendue pour considérer le cas du SU avec possibilité de perception du spectre en bande large ainsi qu'un algorithme pour accéder au spectre, visant à réduire la probabilité de détection manquée. Les résultats démontrent que, comparée à la perception du spectre en bande étroite, la perception du spectre en bande large offre de meilleurs débits aux SU et PU sur toute la plage de facteurs d'activité du PU et le débit combiné est en augmentation monotonique avec le nombre de sous-porteurs. Il est aussi confirmé que, pour la perception du spectre en bande large, le seuil pour la maximisation de capacité est plus performant que les seuils bayésiens pour permettre une contrainte beaucoup plus stricte sur les débits guarantis au PU tout en fournissant de meilleurs débits au PU et SU.