Optimal multi-channel cooperative sensing in cognitive radio networks

Fan, Rongfei; Jiang, Hai

doi:10.1109/twc.2010.03.090467

Cited by 252 publications

(162 citation statements)

References 12 publications

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“…ED ED e fa m p p p  (6) When the composite received signal consists of a large number of plane waves, for some types of scattering environments, the received signal has a Rayleigh distribution. If the signal amplitude follows a Rayleigh distribution, then the SNR follows an exponential probability density function (PDF) which is given by [15].…”

Section: Edmentioning

confidence: 99%

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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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Section: Edmentioning

confidence: 99%

“…Cooperative spectrum sensing (CSS) has thus been widely studied for quick and reliable detection [4]- [6].…”

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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“…The centralized model is built around a singular network-wide authoritative entity called the fusion center (FC) [47,[53][54][55][56][57][58][59][60]. Nodes under this paradigm carry out spectrum-sensing routines independently and send their individual local results to the FC through a common report channel.…”

Section: Cooperation Architecturesmentioning

confidence: 99%

Adaptive Communication Techniques for the Internet of Things

Roussos

2013

JSAN

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Abstract:The vision for the Internet of Things (IoT) demands that material objects acquire communications and computation capabilities and become able to automatically identify themselves through standard protocols and open systems, using the Internet as their foundation. Yet, several challenges still must be addressed for this vision to become a reality. A core ingredient in such development is the ability of heterogeneous devices to communicate adaptively so as to make the best of limited spectrum availability and cope with competition which is inevitable as more and more objects connect to the system. This survey provides an overview of current developments in this area, placing emphasis on wireless sensor networks that can provide IoT capabilities for material objects and techniques that can be used in the context of systems employing low-power versions of the Internet Protocol (IP) stack. The survey introduces a conceptual model that facilitates the identification of opportunities for adaptation in each layer of the network stack. After a detailed discussion of specific approaches applicable to particular layers, we consider how sharing information across layers can facilitate further adaptation. We conclude with a discussion of future research directions.

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“…An optimal multiband joint detection for spectrum sensing is proposed in Fan and Jiang [2010] and Quan et al [2009]. The spectrum sensing problem is formulated as a class of optimization problems about sensing threshold, which maximizes the aggregated throughput of CR under some constraints on the interference to PU.…”

Section: Introductionmentioning

confidence: 99%

Joint optimization of sensing threshold and transmission power in wideband cognitive radio with energy detection

Liu

Jia

et al. 2013

Radio Science

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[1] In this paper, we consider a wideband cognitive radio system that operates over multiple idle subchannels. A joint optimization of sensing threshold and transmission power is proposed, which maximizes the total throughput subject to the constraints on the total interference, the total power, and the probabilities of false alarm and detection of each subchannel. An alternative joint optimization is proposed, which minimizes the total interference under the constraint of the total throughput. The bilevel optimization method is used to solve the proposed optimization problems with a minimized iteration complexity. The mixed-variable optimization problem is divided into two single-variable convex optimization subproblems: the upper level for threshold optimization and the lower level for power optimization. Weighed cooperative sensing is proposed to maximize the detection probability by choosing the optimal weighed factors. The simulations show that the proposed joint optimization algorithm can achieve desirable improvement on the throughput of cognitive radio at the same interference level to primary user, or vice versa within the limits on the probabilities of false alarm and miss detection, and the weighed cooperative sensing can considerably improve sensing performance compared with the unweighed cooperative sensing and single-user sensing.

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Optimal multi-channel cooperative sensing in cognitive radio networks

Cited by 252 publications

References 12 publications

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

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

Adaptive Communication Techniques for the Internet of Things

Joint optimization of sensing threshold and transmission power in wideband cognitive radio with energy detection

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