Cooperative Spectrum Sensing for Cognitive Radios under Bandwidth Constraints

Sun, Changyin; Zhang, Wei; Letaief, Khaled B.

doi:10.1109/wcnc.2007.6

Cited by 359 publications

(232 citation statements)

References 13 publications

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“…Censoring has been considered in the context of wireless sensor networks and cognitive radios [13], [15]- [18] and shown to be effective in saving energy. The design of censoring regions under different optimization settings related to the detection performance has been considered in [15]- [18] for minimization of the miss detection probability with constraints on the false alarm rate and the network energy consumption.…”

mentioning

confidence: 99%

Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

2011

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Abstract-Reliability and energy consumption in detection are key objectives for distributed spectrum sensing in cognitive sensor networks. In conventional distributed sensing approaches, although the detection performance improves with the number of radios, so does the network energy consumption. We consider a combined sleeping and censoring scheme as an energy efficient spectrum sensing technique for cognitive sensor networks. Our objective is to minimize the energy consumed in distributed sensing subject to constraints on the detection performance, by optimally choosing the sleeping and censoring design parameters. The constraint on the detection performance is given by a minimum target probability of detection and a maximum permissible probability of false alarm. Depending on the availability of prior knowledge about the probability of primary user presence, two cases are considered. The case where a priori knowledge is not available defines the blind setup; otherwise the setup is called knowledge-aided. By considering a sensor network based on IEEE 802.15.4/ZigBee radios, we show that significant energy savings can be achieved by the proposed scheme.Index Terms-Cognitive sensor networks, detection and fusion performance, distributed spectrum sensing.

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confidence: 99%

Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

2011

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“…2) Sensing Performance Analysis: Different from [9] and [10], we assume that all the channels experience Rayleigh fading and consider the influence of the inter-user channels.…”

Section: B Double Thresholds Methodsmentioning

confidence: 99%

“…If the number of SUs is very large, the bandwidth to transmit the detecting results may tend to be enormous. Double thresholds method is adopted in [9] to allow the SUs with enough information to report the sensing results to the cluster head merely. [10] has provided a further research in multi-cluster case.…”

Section: Introductionmentioning

confidence: 99%

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Dynamical clustering cooperative spectrum sensing with bandwidth constraints in CR systems

Wang

Bai

et al. 2014

16th International Conference on Advanced Communication Technology

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Abstract-Cooperative spectrum sensing (CSS) scheme is employed to detect the primary user (PU) more quickly and accurately in cognitive radio (CR) systems. The performance of CSS scheme is usually significantly degraded when the inter-user and reporting channels experience path loss or deep fading. Meanwhile, the reporting channels are usually bandwidth limited. When the number of the secondary users (SUs) is very large, the sensing results need enormous bandwidth in the transmission. To solve this problem, a dynamical clustering CSS scheme with bandwidth constraints is proposed, where the SUs with better reporting channels are chosen as the cluster heads and the other SUs are dynamically grouped according to the inter-user channel conditions. In each cluster, the proposed scheme adopts the double thresholds scheme to make sure that only the SUs with high quality can send their sensing results. Simulation results demonstrate that the average number of sensing bits of the proposed scheme decreases greatly with a little loss in the sensing performance. This advantage is more obvious compared with the STBC based cooperative spectrum sensing scheme.

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“…1 shows the arrangement of centralized cooperative spectrum sensing in which multiple SUs collaborate with each other and send their local sensing information to the FC or base station. All the local sensing information from individual SU will be processed at fusion center with one of the hard decision fusion rule and finally an overall decision will be given by fusion center, whether to go with spectrum access or not [16]. …”

Section: B Centralized Cooperative Spectrum Sensing (Ccss)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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Cooperative Spectrum Sensing for Cognitive Radios under Bandwidth Constraints

Cited by 359 publications

References 13 publications

Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

Energy-Efficient Distributed Spectrum Sensing for Cognitive Sensor Networks

Dynamical clustering cooperative spectrum sensing with bandwidth constraints in CR systems

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

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