Triple-Threshold Cooperative Spectrum Sensing Algorithm Based on Energy Detection

Chen, Chutian; Chen, Yuebin; Qian, Jiwu; Xu, Juan

doi:10.1109/icsai.2018.8599342

Cited by 13 publications

(10 citation statements)

References 4 publications

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“…The cooperative spectrum sensing algorithm based on DE (CSBDE) is proposed by Ref [ 8 ] and the ‘Two-stage’ DE is suggested in Ref [ 7 ]. The two-step spectrum sensing scheme based on cyclostationarity (Cyclostationary-DE) is proposed by Ref [ 9 ] and the ‘VRODE’ is the method which is proposed in Ref [ 10 ]. The ‘ADE’ in Ref [ 11 ] is the adaptive double-threshold energy sensing algorithm.…”

Section: Analysis Of Simulation Experimentsmentioning

confidence: 99%

“…However, it is computationally complex and requires longer sensing time. In Ref [ 9 ], a double-threshold method is applied to perform spectrum sensing, while the local energy detection results are divided into a hard decision and soft decision. Since the final decision is made by the fusion center, this collaborative method is difficult to implement.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive double threshold energy detection based on Markov model for cognitive radio

et al. 2017

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The rapid development in the area of cognitive radio technology leads the society to higher standards of spectrum sensing performance, particularly in low signal-to-noise ratio (SNR) environment. This article proposes an adaptive double-threshold energy sensing method based on Markov model (ADEMM). When using the double-threshold energy sensing method, the modified Markov model that accounts for the time varying characteristic of the channel occupancy was presented to resolve the ‘confused’ channel state. Furthermore, in order to overcome the effect of noise uncertainty, the findings of this article introduce an adaptive double-threshold spectrum sensing method that adjusts its thresholds according to the achievable maximal detection probability. Numerical simulations show that the proposed ADEMM achieves better detection performance than the conventional double-threshold energy sensing schemes, especially in very low SNR region.

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Section: Analysis Of Simulation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive double threshold energy detection based on Markov model for cognitive radio

et al. 2017

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“…The results show that the proposed method increases the probability of detection, working significantly better in OFDM than energy or entropy-based detectors when compared with previous work in [30,[35][36][37]. The simulations were carried out with the same parameters as the literature, and the experimental results show that for low SNR, the system detects the signal for above 90% for −17.5 dB in GMSK and −11 dB for OFDM.…”

Section: Discussionmentioning

confidence: 81%

Detection of Malicious Primary User Emulation Based on a Support Vector Machine for a Mobile Cognitive Radio Network Using Software-Defined Radio

2020

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Mobile cognitive radio networks provide a new platform to implement and adapt wireless cellular communications, increasing the use of the electromagnetic spectrum by using it when the primary user is not using it and providing cellular service to secondary users. In these networks, there exist vulnerabilities that can be exploited, such as the malicious primary user emulation (PUE), which tries to imitate the primary user signal to make the cognitive network release the used channel, causing a denial of service to secondary users. We propose a support vector machine (SVM) technique, which classifies if the received signal is a primary user or a malicious primary user emulation signal by using the signal-to-noise ratio (SNR) and Rényi entropy of the energy signal as an input to the SVM. This model improves the detection of the malicious attacker presence in low SNR without the need for a threshold calculation, which can lead to false detection results, especially in orthogonal frequency division multiplexing (OFDM) where the threshold is more difficult to estimate because the signal limit values are very close in low SNR. It is implemented on a software-defined radio (SDR) testbed to emulate the environment of mobile system modulations, such as Gaussian minimum shift keying (GMSK) and OFDM. The SVM made a previous learning process to allow the SVM system to recognize the signal behavior of a primary user in modulations such as GMSK and OFDM and the SNR value, and then the received test signal is analyzed in real-time to decide if a malicious PUE is present. The results show that our solution increases the detection probability compared to traditional techniques such as energy or cyclostationary detection in low SNR values, and it detects malicious PUE signal in MCRN.

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“…This method is further improved using double threshold and eigen value based double threshold and Sevcik Fractal Dimension. The dynamic threshold method, energy-efficient spectrum sensing, and a few other advanced methods for CSS have been discussed in [9,10,11,12,13].…”

Section: Fig3 Conventional Energy Detection Techniquementioning

confidence: 99%

Spectrum Sensing to Detect N-Number of Primary Users Using N-Number Secondary Users by Applying Support Vector Machine.

Udayamoorthy¹,

Sriniva²

2021

Preprint

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In this paper, a cooperative spectrum sensing (CSS) model is proposed to sense n-number of primary users (PUs) using n-number secondary users (SUs) in a sequence by applying support vector machine (SVM) algorithm using three different kernels namely linear, polynomial and radial basis function (RBF) respectively. In this method, fusion centre (FC) instructs all the SUs through control channel, which PU is to be sensed by sending a pre-defined primary user identification code (PUid) and each SU sense the Kth PU spectrum information and stored in a database at FC. SU transmits a bit ‘0’ or bit ‘1’ along with PU sensing information to the FC to indicate whether it needs a spectrum band to transmit the data or not. SU add two identification codes along with sensing information to the FC which indicates that from which SU the sensing information received and which PU is sensed by the SU. For simulation 500 data samples are used and the simulation results show an accuracy of 96% and false alarm value of 1.3% in classifying the SU sensing information at FC using RBF kernel. Another method is proposed with multiclass classification by applying SVM algorithm using RBF kernel. The confusion region class is classified with zero false alarm percentage and achieves an accuracy of 99.3% in classifying the SU sensing information at FC.

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Triple-Threshold Cooperative Spectrum Sensing Algorithm Based on Energy Detection

Cited by 13 publications

References 4 publications

Adaptive double threshold energy detection based on Markov model for cognitive radio

Adaptive double threshold energy detection based on Markov model for cognitive radio

Detection of Malicious Primary User Emulation Based on a Support Vector Machine for a Mobile Cognitive Radio Network Using Software-Defined Radio

Spectrum Sensing to Detect N-Number of Primary Users Using N-Number Secondary Users by Applying Support Vector Machine.

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