Blind Spectrum Sensing Algorithms for Cognitive Radio Networks

De, Parthapratim; Liang, Ying-Chang

doi:10.1109/tvt.2008.915520

Cited by 62 publications

(52 citation statements)

References 7 publications

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“…Energy detection is the basic sensing method, which was first proposed in [9] and further studied by others [5,6,7,10]. It does not need any information of the signal to be detected and is robust to unknown dispersive channel.…”

Section: Previous Detection Methods 31 Energy Detection Algorithmmentioning

confidence: 99%

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Sensing Algorithm for Cognitive Radio Networks based on Random Data Matrix

Ujjinimatad¹,

Patil²

2013

IJCA

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Signal detection is a fundamental problem in Cognitive radio. In this paper a new statistical test is proposed based on random data matrix (RDM) for detecting the signals in noise, as opposed to the eigenvalue based tests. Among the many spectrum sensing methods, the RDM method detects the primary users without any prior information. The performance of the test is compared with energy detection (ED), covariance absolute value (CAV) and eigenvalue based algorithms through simulation analysis. This sensing algorithm can be used for very low SNR signal detection without requiring the knowledge of signal, channel and noise. Simulations are based on wireless microphone and identically and independently distributed (iid) signals.

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Section: Previous Detection Methods 31 Energy Detection Algorithmmentioning

confidence: 99%

“…Therefore, the signal-tonoise ratio (SNR) of the primary users' signal could be very low. If the secondary user makes an erroneous decision in detection and starts transmitting when the primary users are active, its own signal will interfere with the primary users' signals [5].…”

Section: Introductionmentioning

confidence: 99%

Sensing Algorithm for Cognitive Radio Networks based on Random Data Matrix

Ujjinimatad¹,

Patil²

2013

IJCA

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“…Given the estimated statistical model, spectrum occupancy prediction at future instances is achievable. Such models examine several statistical techniques with a major literature focus on Markov processes [10,45], Poisson processes [12,13], Bayesian models [9,14], neural networks [5,11,46], linear regression [15,16], space vector machine [47], pattern mining [48,49], and dictionary-based prediction [9]. In a sequential prediction framework, these techniques represent different parametrised predictor classes.…”

Section: Statistical Occupancy Modellingmentioning

confidence: 99%

“…High-order Markov chain [84] [47] Pattern mining [48,49] ON/OFF) or infinite set (e.g. real space R) are presented.…”

Section: Prediction Model Selectionmentioning

confidence: 99%

“…Neural networks-based techniques are presented extensively in cognitive radio networks [18,[51][52][53][54][55], with application on spectrum prediction presented in [56,57]. Support vector machines [47], pattern mining [48,49], and dictionary-based prediction [9] were suggested for spectrum prediction and user activity modelling. The surveys in [17,18,54] discuss artificial intelligence and machine learning applications for dynamic spectrum access.…”

Section: Machine Learning-based Techniquesmentioning

confidence: 99%

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Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Eltom

Kandeepan

Evans

et al. 2018

J Wireless Com Network

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Spectrum scarcity due to inefficient utilisation has ignited a plethora of dynamic spectrum access solutions to accommodate the expanding demand for future wireless networks. Dynamic spectrum access systems allow secondary users to utilise spectrum bands owned by primary users if the resulting interference is kept below a pre-designated threshold. Primary and secondary user spectrum occupancy patterns determine if minimum interference and seamless communications can be guaranteed. Thus, spectrum occupancy prediction is a key component of an optimised dynamic spectrum access system. Spectrum occupancy prediction recently received significant attention in the wireless communications literature. Nevertheless, a single consolidated literature source on statistical spectrum occupancy prediction is not yet available in the open literature. Our main contribution in this paper is to provide a statistical prediction classification framework to categorise and assess current spectrum occupancy models. An overview of statistical sequential prediction is presented first. This statistical background is used to analyse current techniques for spectrum occupancy prediction. This review also extends spectrum occupancy prediction to include cooperative prediction. Finally, theoretical and implementation challenges are discussed.

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Fast adjustable spectrum sensing circuit for cognitive radio applications

Vahidpoor

Forooraghi

Fotowat-Ahmady

et al. 2013

Circuit Theory & Apps

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A fast and adjustable spectrum sensing method is proposed using a phase frequency detector for cognitive radio applications. Using this method, spectral sensing can be done in a very short time by a low-cost, low-power circuit, compared with available implemented methods.Different aspects of sensing procedure in different situations are investigated including frequency sweep time and system errors. Accurate operation of this method is verified through simulations, and comparison with other methods shows faster sweep rates.

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Blind Spectrum Sensing Algorithms for Cognitive Radio Networks

Cited by 62 publications

References 7 publications

Sensing Algorithm for Cognitive Radio Networks based on Random Data Matrix

Sensing Algorithm for Cognitive Radio Networks based on Random Data Matrix

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Fast adjustable spectrum sensing circuit for cognitive radio applications

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