Robust Distributed Spectrum Sensing in Cognitive Radio Networks

Chen, Ruiliang; Park, Jung‐Min; Bian, Kaigui

doi:10.1109/infocom.2008.251

Cited by 427 publications

(191 citation statements)

References 5 publications

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“…Most of the existing data fusion techniques rely on using a fixed number of samples, but there are also techniques that use a variable number of samples [23].…”

Section: Partial Cooperationmentioning

confidence: 99%

Spectrum Sensing Framework for Cognitive Radio Networks

Gavrilovska

Atanasovski

2011

Wireless Pers Commun

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Spectrum sensing feature of cognitive radio devices represents a cornerstone characteristic facilitating real-time and accurate spectrum occupancy measurements in cognitive radio networks. It practically enables the cognitive radio devices to detect vacant spectrum holes and use them for their communication purposes. There are numerous spectrum sensing methods proposed in the literature ranging from local based ones to cooperative strategies among several devices increasing the confidence level of the detected spectrum. This paper gives a general spectrum sensing framework for cognitive radio networks, classifies and explores different spectrum sensing techniques and approaches and shows practical examples, from authors' own experience, of realized spectrum sensing engines and strategies along with some obtained results.

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“…Most of the existing data fusion techniques rely on using a fixed number of samples, but there are also techniques that use a variable number of samples [23].…”

Section: Partial Cooperationmentioning

confidence: 99%

Spectrum Sensing Framework for Cognitive Radio Networks

Gavrilovska

Atanasovski

2011

Wireless Pers Commun

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“…However, prior knowledge of channel usage is not readily available in reality. Reference [6] proposed a weighted order probability ratio test method based on prestige in order to detect malicious cognitive users and achieve reliable spectrum sensing in the joint spectrum sensing process. However, the algorithm needs to know the prior probabilities of each cognitive user's perceived results, resulting in the computational complexity and communication overhead being too large.…”

Section: Introductionmentioning

confidence: 99%

Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System

2017

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Collaborative spectral sensing can fuse the perceived results of multiple cognitive users, and thus will improve the accuracy of perceived results. However, the multi-source features of the perceived results result in security problems in the system. When there is a high probability of a malicious user attack, the traditional algorithm can correctly identify the malicious users. However, when the probability of attack by malicious users is reduced, it is almost impossible to use the traditional algorithm to correctly distinguish between honest users and malicious users, which greatly reduces the perceived performance. To address the problem above, based on the β function and the feedback iteration mathematical method, this paper proposes a malicious user identification algorithm under multi-channel cooperative conditions (β-MIAMC), which involves comprehensively assessing the cognitive user's performance on multiple sub-channels to identify the malicious user. Simulation results show under the same attack probability, compared with the traditional algorithm, the β-MIAMC algorithm can more accurately identify the malicious users, reducing the false alarm probability of malicious users by more than 20%. When the attack probability is greater than 7%, the proposed algorithm can identify the malicious users with 100% certainty.

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“…Either case could potentially cause interference to legitimate secondary users and result in under utilization of fallow licensed spectrum. We consider the Byzantine failure problem [12] from the perspective of data fusion techniques. This problem causes the decrease in True Positive Ratio.…”

Section: Introductionmentioning

confidence: 99%

“…Either case could potentially cause interference to legitimate secondary users and result in underutilization of fallow licensed spectrum. We consider the Byzantine failure problem [12] Rest of the paper organized as follows, Section II explains the WSPRT Technique, Section III explains the System Model, Section IV explains Simulation Results and Section V concludes the paper.…”

Section: Introductionmentioning

confidence: 99%

Detection of PUE Attack by SPARS Model using WSPRT

Sairam¹,

Ramakrishnaiah²

2015

IJSEA

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Cognitive radio is a system which improves the utilization of the spectrum by sensing the white spaces in its vicinity. This sensed information will be utilized by the Secondary User (SU) to transmit the data. But some of the malicious users attacks th system by generating the signal same as that of the primary transmitter. The attack caused by generating the signal same as t primary transmitter is called as Primary User Emulation Attack (PUEA). In this paper the Signal Activity Pattern Acquisition Reconstruction System (SPARS) is used to detect the attack. But this system suffers from low True Positive Rate. To incr positive rate or sensitivity a new technique was proposed called as Weighted Sequential Probability Ratio Test (WSPRT). By improving the true positive rate or sensitivity, the detection capability of the system will be improved.

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Robust Distributed Spectrum Sensing in Cognitive Radio Networks

Cited by 427 publications

References 5 publications

Spectrum Sensing Framework for Cognitive Radio Networks

Spectrum Sensing Framework for Cognitive Radio Networks

Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System

Detection of PUE Attack by SPARS Model using WSPRT

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