On the Performance of Eigenvalue-Based Spectrum Sensing for Cognitive Radio

Kortun, Ayse; Ratnarajah, Tharmalingam; Sellathurai, Mathini; Zhong, Caijun

doi:10.1109/dyspan.2010.5457844

Cited by 23 publications

(24 citation statements)

References 16 publications

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“…Kortun et al [1] improved the model by finding an exact threshold and an approximate closed-form performance that agreed well with the empirical results. Independently, Penna et al [7] derived work similar to [1] and then further analyzed the probability of a miss in [10].…”

Section: ⅰ Introductionsupporting

confidence: 53%

“…Much recent work has focused on eigenvalue-based spectrum sensing methods for cognitive radio networks (CRNs) [1], [2], where researchers have applied innovations from random matrix theory to calculate the probability of a false alarm as a function of a threshold. This work has also employed many kinds of test statistics, such as the ratio of the largest and the smallest eigenvalues or the ratio of the average and the smallest eigenvalues of the sample covariance matrix.…”

Section: ⅰ Introductionmentioning

confidence: 99%

“…Independently, Penna et al [7] derived work similar to [1] and then further analyzed the probability of a miss in [10]. However, the method used in [10] still requires knowledge of noise level and the parameters of primary user (PU) signals and channels, which is impractical in CRN scenarios.…”

Section: ⅰ Introductionmentioning

confidence: 99%

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Performance of Spiked Population Models for Spectrum Sensing

Le¹,

Kong

2012

Journal of electromagnetic engineering and science

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In order to improve sensing performance when the noise variance is not known, this paper considers a so-called blind spectrum sensing technique that is based on eigenvalue models. In this paper, we employed the spiked population models in order to identify the miss detection probability. At first, we try to estimate the unknown noise variance based on the blind measurements at a secondary location. We then investigate the performance of detection, in terms of both theoretical and empirical aspects, after applying this estimated noise variance result. In addition, we study the effects of the number of SUs and the number of samples on the spectrum sensing performance. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Ⅰ. IntroductionMuch recent work has focused on eigenvalue-based spectrum sensing methods for cognitive radio networks (CRNs) [1], [2], where researchers have applied innovations from random matrix theory to calculate the probability of a false alarm as a function of a threshold. This work has also employed many kinds of test statistics, such as the ratio of the largest and the smallest eigenvalues or the ratio of the average and the smallest eigenvalues of the sample covariance matrix. Yonghong and Ying-chang's model in [2] requires a number of samples, N, and a number of secondary users (SUs), K, approaching infinity. A significant gap exists between the simulations and analytical results.Kortun et al.[1] improved the model by finding an exact threshold and an approximate closed-form performance that agreed well with the empirical results. Independently, Penna et al. [7] derived work similar to [1] and then further analyzed the probability of a miss in [10]. However, the method used in [10] still requires knowledge of noise level and the parameters of primary user (PU) signals and channels, which is impractical in CRN scenarios. To our knowledge, not much research has been carried out regarding the relationship between the threshold and the probability of a miss when using blind spectrum sensing techniques. This paper considers a novel blind spectrum sensing technique for CRNs, where we analyze a threshold with a constraint on the probability of a miss. CRNs are composed of a specific bandwidth, including multiple PUs and the number of SUs. Based on a variety of research related to the distribution of eigenvalues [3], [4], we apply the results of random matrix theory to the spectrum sensing model. In fact, the practical model is related to a finite number of samples and SUs, while these parameters are infinite in random matrix theory. Therefore, we exploit the limit distribution for approximate results in our applied spectrum sensing model. Furthermore, the observations referred to in this spectrum sensing model undergo interference with by ...

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Section: ⅰ Introductionsupporting

confidence: 53%

Section: ⅰ Introductionmentioning

confidence: 99%

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Performance of Spiked Population Models for Spectrum Sensing

Le¹,

Kong

2012

Journal of electromagnetic engineering and science

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“…Despite the simplicity, its detection accuracy is prone to noise uncertainty. Eigenvalue-based detection, on the other hand, examines the eigenvalue of received signal covariance and does not share the drawback of energy detection [31]. Although energy detection is, in many cases, outperformed by the eigenvalue-based approach [30], its remarkably low computational complexity and flexibility makes it one of the most popular measures, especially when spectrum sensing is purely used for probing the interference level rather than the detection of particular signal sources.…”

Section: Summary Of Adaptive Phymentioning

confidence: 99%

“…The detection threshold is key to the performance of this sensing method. The probability of false alarms were employed to deduce the appropriate threshold in a way that its relationship with signal and noise property is eliminated [31].…”

Section: Eigenvalue-based Detectionmentioning

confidence: 99%

Adaptive Communication Techniques for the Internet of Things

Roussos

2013

JSAN

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Abstract:The vision for the Internet of Things (IoT) demands that material objects acquire communications and computation capabilities and become able to automatically identify themselves through standard protocols and open systems, using the Internet as their foundation. Yet, several challenges still must be addressed for this vision to become a reality. A core ingredient in such development is the ability of heterogeneous devices to communicate adaptively so as to make the best of limited spectrum availability and cope with competition which is inevitable as more and more objects connect to the system. This survey provides an overview of current developments in this area, placing emphasis on wireless sensor networks that can provide IoT capabilities for material objects and techniques that can be used in the context of systems employing low-power versions of the Internet Protocol (IP) stack. The survey introduces a conceptual model that facilitates the identification of opportunities for adaptation in each layer of the network stack. After a detailed discussion of specific approaches applicable to particular layers, we consider how sharing information across layers can facilitate further adaptation. We conclude with a discussion of future research directions.

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Spectrum Usage Models for the Analysis, Design and Simulation of Cognitive Radio Networks

López-Benítez

Casadevall

2012

Lecture Notes in Electrical Engineering

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ADVERTIMENT. La consulta d'aquesta tesi queda condicionada a l'acceptació de les següents condicions d'ús: La difusió d'aquesta tesi per mitjà del servei TDX (www.tesisenxarxa.net) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d'investigació i docència. No s'autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d'un lloc aliè al servei TDX. No s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora.ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del servicio TDR (www.tesisenred.net) ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR. No se autoriza la presentación de su contenido en una ventana o marco ajeno a TDR (framing). Esta reserva de derechos afecta tanto al resumen de presentación de la tesis como a sus contenidos. En la utilización o cita de partes de la tesis es obligado indicar el nombre de la persona autora. WARNING.On having consulted this thesis you're accepting the following use conditions: Spreading this thesis by the TDX (www.tesisenxarxa.net) service has been authorized by the titular of the intellectual property rights only for private uses placed in investigation and teaching activities. Reproduction with lucrative aims is not authorized neither its spreading and availability from a site foreign to the TDX service. Introducing its content in a window or frame foreign to the TDX service is not authorized (framing). This rights affect to the presentation summary of the thesis as well as to its contents. In the using or citation of parts of the thesis it's obliged to indicate the name of the author Essentially, all models are wrong, but some are useful.George E. P. BoxTo my beloved family for their unconditional love and support.A mi querida familia por su cariño y apoyo incondicionales. 0 ABSTRACTThe owned spectrum allocation policy, in use since the early days of modern radio communications, has been proven to effectively control interference among radio communication systems and simplify the design of hardware for use at a known and fixed range of frequencies. However, the overwhelming proliferation of new operators, innovative services and wireless technologies during the last years has resulted, under this static regulatory regime, in the depletion of all spectrum bands with commercially attractive radio propagation characteristics. The vast majority of spectrum regarded as usable has already been allocated, thus...

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On the Performance of Eigenvalue-Based Spectrum Sensing for Cognitive Radio

Cited by 23 publications

References 16 publications

Performance of Spiked Population Models for Spectrum Sensing

Performance of Spiked Population Models for Spectrum Sensing

Adaptive Communication Techniques for the Internet of Things

Spectrum Usage Models for the Analysis, Design and Simulation of Cognitive Radio Networks

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