A Robust Cooperative Spectrum Sensing Based on Kullback-Leibler Divergence

Vu-Van, Hiep; Koo, Insoo

doi:10.1587/transcom.e95.b.1286

Cited by 12 publications

(10 citation statements)

References 6 publications

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“…The spectrum sensing operation of each SU in a particular spectrum results in deciding hypotheses H 1 and H 0 as [18,32,39] …”

Section: System Modelmentioning

confidence: 99%

“…According to the central limit theorem (CLT), if the number of samples is large enough, Z j i is asymptotically normally distributed under both H 0 and H 1 hypotheses [18,32] as…”

Section: System Modelmentioning

confidence: 99%

“…The CSS protection scheme, discussed in [31], is used for the detection of the always yes and always no MUs, where outlier detection technique is used. Kullback-Leibler divergencebased CSS is proposed in [32] to protect the CSS from the spectrum sensing data falsification (SSDF) attack [33] of the always yes and always no MUs and work fine in case of a large number of MUs. In the hard decision fusion (HDF) as in [8,[34][35][36], SUs send a hard binary decision of the PU channel to the FC.…”

Section: Introductionmentioning

confidence: 99%

“…In comparison with [39], this work optimizes the detection, false alarm, and error results when MUs take low and high SNR of the channel in comparison with NSUs. The proposed scheme is tested at different levels of SNR and increasing number of cooperative users with simple soft decision fusion (SDF) and hard decision fusion (HDF) schemes in [28][29][30][31][32][33][34][35][36]. Simulation results at different levels of cooperative SUs and various SNR levels confirmed that with the use of the one-to-many neighbor distance-and z-score-based GA, the system is able to produce more sophisticated detection results for the HDF schemes in the presence of MUs.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the proposed method outcomes are verified and tested against the existence of an opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious user (ANMU) [32,39]. An AYMU provides a high-energy signal to the FC irrespective of the actual PU spectrum status, thus increasing false alarm probability and reducing throughput for the SUs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

Gul

Qureshi

Elahi

et al. 2018

International Journal of Antennas and Propagation

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In cognitive radio network (CRN), secondary users (SUs) try to sense and utilize the vacant spectrum of the legitimate primary user (PU) in an efficient manner. The process of cooperation among SUs makes the sensing more authentic with minimum disturbance to the PU in achieving maximum utilization of the vacant spectrum. One problem in cooperative spectrum sensing (CSS) is the occurrence of malicious users (MUs) sending false data to the fusion center (FC). In this paper, the FC takes a global decision based on the hard binary decisions received from all SUs. Genetic algorithm (GA) using one-to-many neighbor distance along with z-score as a fitness function is used for the identification of accurate sensing information in the presence of MUs. The proposed scheme is able to avoid the effect of MUs in CSS without identification of MUs. Four types of abnormal SUs, opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious user (ANMU), are discussed in this paper. Simulation results show that the proposed hard fusion scheme has surpassed the existing hard fusion scheme, equal gain combination (EGC), and maximum gain combination (MGC) schemes by employing GA.

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“…The spectrum sensing operation of each SU in a particular spectrum results in deciding hypotheses H 1 and H 0 as [18,32,39] …”

Section: System Modelmentioning

confidence: 99%

“…According to the central limit theorem (CLT), if the number of samples is large enough, Z j i is asymptotically normally distributed under both H 0 and H 1 hypotheses [18,32] as…”

Section: System Modelmentioning

confidence: 99%