Centralized and decentralized cooperative spectrum sensing in cognitive radio networks: A novel approach

Noorshams, Nima; Malboubi, Mehdi; Bahai, A.

doi:10.1109/spawc.2010.5670998

Cited by 33 publications

(13 citation statements)

References 8 publications

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“…Furthermore, a new decentralized cooperative spectrum sensing algorithm is proposed [18]. The local information of secondary users is appropriately combined to guarantee a reliable communication.…”

Section: Related Workmentioning

confidence: 99%

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Cooperative Cognitive Intelligence for Internet of Vehicles

Paul

Daniel

Ahmad

et al. 2017

IEEE Systems Journal

133

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To resolve the contradictions between the increasing demand of vehicular wireless applications and the shortage of spectrum resources, high mobility, short link lifetime, and spectrum efficiency, a novel cognitive radio (CR) and efficient management of spectrum in vehicular communication is required. Therefore, to exhibit the importance of spectral efficiency, a system model is proposed for cooperative centralized and distributed spectrum sensing in vehicular networks. The proposed architecture is used to minimize both the spectral scarcity and high mobility issues. Furthermore, we analyze the decision fusion techniques in cooperative spectrum sensing for vehicular networks. In addition, a system model is designed for decision fusion techniques using renewal theory, and then, we analyze the probability of detection of primary channel and the average waiting time for CR user or secondary user in PU transmitter. Finally, mathematical analysis is performed to check the probability of detection and false alarm. The results show that the cooperative cognitive model is more suitable for vehicular networks that minimize interference and hidden PU problem.Index Terms-Centralized spectrum sensing, cognitive radio (CR), decision fusion controller (DFC), distributed spectrum sensing.

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Section: Related Workmentioning

confidence: 99%

“…The interference problem for HMM involves determining the input data and yielding output probability of distribution. The time consumption in processing those data is critical [18]. The aforementioned scenario depicts that HMM decision fusion techniques would be vulnerable to vehicular networks.…”

Section: ) Hmmmentioning

confidence: 99%

Cooperative Cognitive Intelligence for Internet of Vehicles

Paul

Daniel

Ahmad

et al. 2017

IEEE Systems Journal

133

View full text Add to dashboard Cite

show abstract

“…The individual sensing information will be exchanged among the collaborating users to decide on the presence or absence of PU in a collaborative way. Each SU will make their final decision based on the sensing information it received from other SUs using appropriate hard decision fusion rule [17]. Fig.…”

Section: Decentralized Cooperative Spectrum Sensing (Dcss)mentioning

confidence: 99%

Optimal Hard Decision Fusion Rule for Centralized and Decentralized Cooperative Spectrum Sensing in Cognitive Radio Networks

Rauniyar¹,

Jang²,

Shin³

2015

JACN

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Abstract-Cognitive radio (CR) is the key technology for unlicensed secondary users (SUs) to exploit the unused spectrum of primary users (PUs) via opportunistic spectrum access. In CR networks, SUs can be coordinated to perform cooperative spectrum sensing (CSS) to achieve higher detection accuracy. The fusion center in centralized CSS collects all the individual local spectrum sensing information from the respective SUs and combines the local spectrum sensing result by applying appropriate fusion rule to give a final collaborative decision whether to proceed with spectrum access or not. Basically, there exist two kinds of detection errors (i.e., miss-detection error and false-alarm error) in spectrum sensing, which degrade the sensing performance severely. Aiming to minimize the probability of total detection errors, we first dissect different hard decision fusion rule and we establish that "Half-Voting (HV)" rule is the optimal fusion rule applied to the detector for centralized CSS. Next, the performance of different hard decision fusion rule in AWGN channel and Rayleigh fading channel is carried out in MATLAB to find out the optimal hard decision fusion rule in centralized and decentralized CSS.

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“…In [19] and [20], the Baum-Welch algorithm [21], [22] has been applied to estimate the parameters of the HMM model, so that the fusion center can achieve more accurate spectrum state decisions. However, in [19] and [20], all the secondary users are assumed to be honest and the security problem is not considered.…”

Section: Introductionmentioning

confidence: 99%

“…In(20),(21) and(22), a i,j , bm j , π i and c m are the estimates in λ 5. Usually, the estimates of the proposed algorithm converge to reasonably accurate values in less than 30 iterations.…”

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confidence: 99%

HMM-Based Malicious User Detection for Robust Collaborative Spectrum Sensing

Dai

Ning

2013

IEEE J. Select. Areas Commun.

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Collaborative spectrum sensing improves the spectrum state estimation accuracy but is vulnerable to the potential attacks from malicious secondary cognitive radio (CR) users, and thus raises security concerns. One promising malicious user detection method is to identify their abnormal statistical spectrum sensing behaviors. From this angle, two hidden Markov models (HMMs) corresponding to honest and malicious users respectively are adopted in this paper to characterize their different sensing behaviors, and malicious user detection is achieved via detecting the difference in the corresponding HMM parameters. To obtain the HMM estimates, an effective inference algorithm that can simultaneously estimate two HMMs without requiring separated training sequences is also developed. By using these estimates, high malicious user detection accuracy can be achieved at the fusion center, leading to more robust and reliable collaborative spectrum sensing performance (substantially enlarged operational regions) in the presence of malicious users, as compared to the baseline approaches. Different fusion methods are also discussed and compared.

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Centralized and decentralized cooperative spectrum sensing in cognitive radio networks: A novel approach

Cited by 33 publications

References 8 publications

Cooperative Cognitive Intelligence for Internet of Vehicles

Cooperative Cognitive Intelligence for Internet of Vehicles

Optimal Hard Decision Fusion Rule for Centralized and Decentralized Cooperative Spectrum Sensing in Cognitive Radio Networks

HMM-Based Malicious User Detection for Robust Collaborative Spectrum Sensing

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