Sequential detection based cooperative spectrum sensing algorithms in cognitive radio

Jayaprakasam, ArunKumar; Sharma, Vinod

doi:10.1109/ukiwcws.2009.5749426

Cited by 16 publications

(8 citation statements)

References 25 publications

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“…Let us consider the model used in [5] for observations taken by the SUs. Consider the slotted system, where the channel is sensed in the beginning of each slot.…”

Section: A System Model For Uncontaminated Datamentioning

confidence: 99%

“…Let f 0 (x) and f 1,l (x) be the nominal density functions of X k,l before the change point and after the change point, respectively. Now we present DualCUSUM algorithm developed in [5] and then robustify it for the case of contaminated data. DualCUSUM Algorithm: 1) Each of the secondary users l, runs CUSUM algorithm based on the test statistic.…”

Section: A System Model For Uncontaminated Datamentioning

confidence: 99%

“…So the density functions of contaminated data with contamination level data are as defined in (4) and (5). These data are processed using designed LFP of Gaussian distributions and results are presented in tables V and VI respectively.…”

Section: B Contamination By High Variance Gaussian and Double Exponementioning

confidence: 99%

“…In the context of cooperative spectrum sensing, Dual-CUSUM algorithm was proposed in [4] and subsequently used in [5] for sequentially detecting the absence or presence of spectral holes. This algorithm consists of two applications of Page's CUSUM [6], one by the SUs and other by the fusion centre.…”

Section: Introductionmentioning

confidence: 99%

“…This algorithm consists of two applications of Page's CUSUM [6], one by the SUs and other by the fusion centre. Status of the channel is decided by the hypothesis testing between H 0 and H 1 which are defined as, H 0 : Presence of spectral hole H 1 : Absence of spectral hole Absence of spectral hole in [5] is either indicated by the change in mean or the change in variance which are parameters of a suitable parametric family of distributions. Suppose the data received by SUs is contaminated due to the presence of small percentage of outliers then, as shown in table II, DualCUSUM algorithm fails to give the desired results which motivated us to propose robust DualCUSUM algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Robust dual cumulative sum algorithm for cooperative spectrum sensing

Kadam

Sharma

Bansal

2013

2013 National Conference on Communications (NCC)

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Spectrum sensing is an important problem in cognitive radio. Outliers present in the channel deteriorate the performance of existing non-robust algorithms. We consider the problem of limiting the influence of outliers in cooperative spectrum sensing techniques. In this work we use Huber's least favorable pair based on mixture model with appropriate nominal distributions in the DualCUSUM algorithm, a sequential change point detection algorithm used for spectrum sensing. We show that proposed robust DualCUSUM algorithm performs better than existing DualCUSUM algorithm in the presence of outliers. It is also shown by simulation results that better performance can be achieved when the design parameter used in obtaining the least favorable pair equals the actual contamination level in the data. A method to generate random numbers which follow least favorable pair of distributions is also discussed.

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“…Let us consider the model used in [5] for observations taken by the SUs. Consider the slotted system, where the channel is sensed in the beginning of each slot.…”

Section: A System Model For Uncontaminated Datamentioning

confidence: 99%

Section: A System Model For Uncontaminated Datamentioning

confidence: 99%

Section: B Contamination By High Variance Gaussian and Double Exponementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust dual cumulative sum algorithm for cooperative spectrum sensing

Kadam

Sharma

Bansal

2013

2013 National Conference on Communications (NCC)

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Detection of Malicious Nodes Using Collaborative Neighbour Monitoring in DSA Networks

Takyi

Zlobinsky

Akuye-Shika

et al. 2022

Cognitive Radio Oriented Wireless Networks and Wireless Internet

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Quickest Change Detection

Veeravalli

Banerjee

2014

Academic Press Library in Signal Processing

154

111

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The problem of detecting changes in the statistical properties of a stochastic system and time series arises in various branches of science and engineering. It has a wide spectrum of important applications ranging from machine monitoring to biomedical signal processing. In all of these applications the observations being monitored undergo a change in distribution in response to a change or anomaly in the environment, and the goal is to detect the change as quickly as possibly, subject to false alarm constraints.In this chapter, two formulations of the quickest change detection problem, Bayesian and minimax, are introduced, and optimal or asymptotically optimal solutions to these formulations are discussed. Then some generalizations and extensions of the quickest change detection problem are described. The chapter is concluded with a discussion of applications and open issues. I. INTRODUCTIONThe problem of quickest change detection comprises three entities: a stochastic process under observation, a change point at which the statistical properties of the process undergo a change, and a decision maker that observes the stochastic process and aims to detect this change in the statistical properties of the process. A false alarm event happens when the change is declared by the decision maker before the change actually occurs. The general objective of the theory of quickest change detection is to design algorithms that can be used to detect the change as soon as possible, subject to false alarm constraints.The quickest change detection problem has a wide range of important applications, including biomedical signal and image processing, quality control engineering, financial markets, link failure detection in communication networks, intrusion detection in computer networks and security systems, chemical or biological warfare agent detection systems (as a protection tool against terrorist attacks), detection of the onset of an epidemic, failure detection in manufacturing systems and large machines, target detection

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Sequential detection based cooperative spectrum sensing algorithms in cognitive radio

Cited by 16 publications

References 25 publications

Robust dual cumulative sum algorithm for cooperative spectrum sensing

Robust dual cumulative sum algorithm for cooperative spectrum sensing

Detection of Malicious Nodes Using Collaborative Neighbour Monitoring in DSA Networks

Quickest Change Detection

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