Relevant bibliographies by topics / Spectrum sensing ; cognitive radio

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Academic literature on the topic 'Spectrum sensing ; cognitive radio'

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Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Spectrum sensing ; cognitive radio"

Vinod Kumar Reddy, M., and K. Arun Kumar. "Cognitive Radio Networks- Spectrum Sensing." CVR Journal of Science & Technology 8, no. 1 (2015): 31–34. http://dx.doi.org/10.32377/cvrjst0806.

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Haykin, S., D. J. Thomson, and J. H. Reed. "Spectrum Sensing for Cognitive Radio." Proceedings of the IEEE 97, no. 5 (2009): 849–77. http://dx.doi.org/10.1109/jproc.2009.2015711.

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Khobragade, A. S., and R. D. Raut. "Hybrid Spectrum Sensing Method for Cognitive Radio." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2683. http://dx.doi.org/10.11591/ijece.v7i5.pp2683-2695.

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With exponential rise in the internet applications and wireless communications, higher and efficient data transfer rates are required. Hence proper and effective spectrum is the need of the hour, As spectrum demand increases there are limited number of bands available to send and receive the data. Optimizing the use of these bands efficiently is one of the tedious tasks. Various techniques are used to send the data at same time, but for that we have to know which bands are free before sending the data. For this purpose various spectrum sensing approaches came with variety of solutions. In this paper the sensing problem is tackled with the use of hybrid spectrum sensing method, This new networking paradox uses the Centralized concept of spectrum sensing and creates one of the most trusted spectrums sensing mechanism. This proposed technique is simulated using MATLAB software.This paper also provides comparative study of various spectrum sensing methodologies

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Kim, Joo-Seok, Hyun-So Lee, Sung-Ho Hwang, Jun-Ki Min, Ki-Hong Kim, and Kyung-Seok Kim. "Development of MAC Function for the Spectrum Sensing based on Cognitive Radio." Journal of the Korea Contents Association 8, no. 8 (2008): 28–36. http://dx.doi.org/10.5392/jkca.2008.8.8.028.

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Dev, Jenish, B. Mathumitha, M. Arul Bharathi, M. Benisha Rachel, A. Anu, and M. Benisha Rachel. "COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO." INTERNATIONAL JOURNAL OF RECENT TRENDS IN ENGINEERING & RESEARCH 05, Special Issue 07 (2019): 145–50. http://dx.doi.org/10.23883/ijrter.conf.20190304.024.mrhue.

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C., Jayasri. "Spectrum Sensing in Cognitive Radio Network Using Cuckoo Search with Energy Heuristic." Journal of Advanced Research in Dynamical and Control Systems 51, SP3 (2020): 339–48. http://dx.doi.org/10.5373/jardcs/v12sp3/20201270.

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S., Vyshnavi Jyothi. "Spectrum Sensing based on Energy Detection for Cognitive Radio Using Poission Traffic." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 2761–68. http://dx.doi.org/10.37200/ijpr/v24i5/pr201979.

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Kaarthik, K., P. T. Sivagurunathan, and S. Sivaranjani. "A REVIEW ON SPECTRUM SENSING METHODS FOR COGNITIVE RADIO NETWORKS." JOURNAL OF ADVANCES IN CHEMISTRY 12, no. 18 (2016): 5053–57. http://dx.doi.org/10.24297/jac.v12i18.5380.

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In Wireless Communication, Radio Spectrum is doing a vital role; for the future need it should use efficient. The existing system, it is not possible to use it efficiently where the allocation of spectrum is done based on fixed spectrum access (FSA) policy. Several surveys prove that it show the way to inefficient use of spectrum. An innovative technique is needed for spectrum utilization effectively. Using Dynamic spectrum access (DSA) policy, available spectrum can be exploited. Cognitive radio arises to be an attractive solution which introduces opportunistic usage of the frequency bands that are not commonly occupied by licensed users. Cognitive radios promote open spectrum allocation which is a clear departure from habitual command and control allocation process for radio spectrum usage. In short, it permits the formation of â€œinfrastructure-lessâ€ joint network clusters which is called Cognitive Radio Networks (CRN). Conversely the spectrum sensing techniques are needed to detect free spectrum. In this paper, different spectrum sensing techniques are analyzed.

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M, Anusha, Srikanth Vemuru, and T. Gunasekhar. "Transmission protocols in Cognitive Radio Mesh Networks." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 6 (2015): 1446. http://dx.doi.org/10.11591/ijece.v5i6.pp1446-1451.

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A Cognitive Radio (CR) is a radio that can adjust its transmission limit based on available spectrum in its operational surroundings. Cognitive Radio Network (CRN) is made up of both the licensed users and unlicensed users with CR enable and disabled radios. CR’S supports to access dynamic spectrum and supports secondary user to access underutilized spectrum efficiently, which was allocated to primary users. In CRN’S most of the research was done on spectrum allocation, spectrum sensing and spectrum sharing. In this literature, we present various Medium Access (MAC) protocols of CRN’S. This study would provide an excellent study of MAC strategies.

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Cui, Chua Nan. "Study of Cognitive Radio Spectrum Sensing Algorithm Based on Compressed Sensing." Applied Mechanics and Materials 602-605 (August 2014): 3639–42. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3639.

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As the latest progress in modern signal processing, Compressed Sensing (CS) has great potential for application in the field of cognitive radio and other fields. In this paper, the basic principle of compressed sensing technology and its application in cognitive radio spectrum perception key technologies are studied, achieving innovative research results. Spectrum sensing technology in cognitive radio

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Dissertations / Theses on the topic "Spectrum sensing ; cognitive radio"

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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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.

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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. 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 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. 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. 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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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.

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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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Books on the topic "Spectrum sensing ; cognitive radio"

author, Tellambura Chintha 1962, and Jiang Hai author, eds. Energy detection for spectrum sensing in cognitive radio. Springer, 2014.

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Atapattu, Saman, Chintha Tellambura, and Hai Jiang. Energy Detection for Spectrum Sensing in Cognitive Radio. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0494-5.

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Andrea, Giorgetti, ed. Cognitive radio techniques: Spectrum sensing, interference mitigation, and localization. Artech House, 2012.

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Fernando, Xavier, Ajmery Sultana, Sattar Hussain, and Lian Zhao. Cooperative Spectrum Sensing and Resource Allocation Strategies in Cognitive Radio Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-73957-1.

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Stefan, Mangold, ed. Cognitive radio for dynamic spectrum access. J. Wiley & Sons, 2009.

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Pandit, Shweta, and Ghanshyam Singh. Spectrum Sharing in Cognitive Radio Networks. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53147-2.

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Zhao, Guodong, Wei Zhang, and Shaoqian Li. Advanced Sensing Techniques for Cognitive Radio. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42784-3.

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Wang, Zhe, and Wei Zhang. Opportunistic Spectrum Sharing in Cognitive Radio Networks. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15542-5.

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Haldorai, Anandakumar, and Umamaheswari Kandaswamy. Intelligent Spectrum Handovers in Cognitive Radio Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15416-5.

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Yi, Changyan, and Jun Cai. Market-Driven Spectrum Sharing in Cognitive Radio. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29691-3.

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Book chapters on the topic "Spectrum sensing ; cognitive radio"

Bruno, Joseph M., Yariv Ephraim, Brian L. Mark, and Zhi Tian. "Spectrum Sensing Using Markovian Models." In Handbook of Cognitive Radio. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-1389-8_2-1.

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Hattab, Ghaith, and Danijela Cabric. "Spectrum Sensing, Measurement, and Modeling." In Handbook of Cognitive Radio. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-1389-8_5-1.

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Xin, Yan, and Lifeng Lai. "Sequential Methods for Spectrum Sensing." In Handbook of Cognitive Radio. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-1389-8_9-1.

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Bruno, Joseph M., Yariv Ephraim, Brian L. Mark, and Zhi Tian. "Spectrum Sensing Using Markovian Models." In Handbook of Cognitive Radio. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-1394-2_2.

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Hattab, Ghaith, and Danijela Cabric. "Spectrum Sensing, Measurement, and Modeling." In Handbook of Cognitive Radio. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-1394-2_5.

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Xin, Yan, and Lifeng Lai. "Sequential Methods for Spectrum Sensing." In Handbook of Cognitive Radio. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-1394-2_9.

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Fahim, Amr. "Wideband Spectrum Sensing Techniques." In Radio Frequency Integrated Circuit Design for Cognitive Radio Systems. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11011-0_4.

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Tellambura, Chintha. "Spectrum Sensing Methods and Their Performance." In Handbook of Cognitive Radio. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-1389-8_6-1.

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Gao, Yue, and Yuan Ma. "Spectrum Sensing, Database, and Its Hybrid." In Handbook of Cognitive Radio. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-1389-8_8-1.

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Tellambura, Chintha. "Spectrum Sensing Methods and Their Performance." In Handbook of Cognitive Radio. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-1394-2_6.

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Conference papers on the topic "Spectrum sensing ; cognitive radio"

Wang, Weifang. "Spectrum Sensing for Cognitive Radio." In 2009 3rd International Symposium on Intelligent Information Technology Application Workshops (IITAW). IEEE, 2009. http://dx.doi.org/10.1109/iitaw.2009.49.

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Alemseged, Yohannes D., and Hiroshi Harada. "Spectrum sensing for cognitive radio." In 2009 IEEE Radio and Wireless Symposium (RWS). IEEE, 2009. http://dx.doi.org/10.1109/rws.2009.4957354.

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Guibène, Wael, and Dirk Slock. "Spectrum sensing for cognitive radio exploiting spectral masks." In the 4th International Conference. ACM Press, 2011. http://dx.doi.org/10.1145/2093256.2093310.

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Arshad, K., and K. Moessner. "Collaborative Spectrum Sensing for Cognitive Radio." In 2009 IEEE International Conference on Communications Workshops. IEEE, 2009. http://dx.doi.org/10.1109/iccw.2009.5208032.

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Muchandi, Niranjan, and Rajashri Khanai. "Cognitive radio spectrum sensing: A survey." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7755301.

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Azim, Ali Waqar, Syed Safwan Khalid, and Shafayat Abrar. "Statistical Spectrum Sensing in Cognitive Radio." In 2012 10th International Conference on Frontiers of Information Technology (FIT 2012). IEEE, 2012. http://dx.doi.org/10.1109/fit.2012.34.

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Zeng, Yonghong, and Ying-Chang Liang. "Robust spectrum sensing in cognitive radio." In 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops (PIMRC Workshops). IEEE, 2010. http://dx.doi.org/10.1109/pimrcw.2010.5670361.

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Seshukumar, K., R. Saravanan, and M. S. Suraj. "Spectrum sensing review in cognitive radio." In 2013 International Conference on Emerging Trends in VLSI, Embedded System, Nano Electronics and Telecommunication System (ICEVENT). IEEE, 2013. http://dx.doi.org/10.1109/icevent.2013.6496549.

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Cui, Tao, Jia Tang, Feifei Gao, and Chintha Tellambura. "Blind Spectrum Sensing in Cognitive Radio." In Networking Conference (WCNC). IEEE, 2010. http://dx.doi.org/10.1109/wcnc.2010.5506471.

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Meena, M., F. Bhagari, and V. Rajendran. "Spectrum Sensing Using Cognitive Radio Technology." In 2017 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2017. http://dx.doi.org/10.1109/iccsp.2017.8286672.

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Reports on the topic "Spectrum sensing ; cognitive radio"

Jayaweera, Sudharman. Machine Learning-Aided, Robust Wideband Spectrum Sensing for Cognitive Radios. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada625246.

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