Data and decision fusion for distributed spectrum sensing in cognitive radio networks

Kattepur, Ajay; Hoang, Ann Tuan; Liang, Ying-Chang; Er, Meng Joo

doi:10.1109/icics.2007.4449547

Cited by 22 publications

(14 citation statements)

References 4 publications

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“…In both cases, receivers fuse user information and make a final decision H 0 or H 1 to declare presence of primary users in observed frequency band. There are many ways to combine or fuse individual decisions; most commonly used in literature are OR (1-out-of-n) and AND (n-out-of-n) fusion rules [22], [18], [23].…”

Section: Collaborative Spectrum Sensing Techniquesmentioning

confidence: 99%

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Collaborative Spectrum Sensing for Cognitive Radio

Arshad

Moessner

2009

2009 IEEE International Conference on Communications Workshops

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Today's wireless communication systems follow fixed spectrum assignment policies which leads to overall inefficient spectrum use. Further, spectrum scarcity is an issue for operators with emerging mobile services and large number of users with even higher capacity requirements. This inefficiency and scarcity in spectrum usage necessitates a new paradigm for communications such as utilising available spectrum opportunistically. Cognitive Radio (CR) is an enabling technology having potential to increase spectrum utilisation and provide desired interference protection to licenced users. This can be done by detection of spectrum opportunities by secondary users. Due to channel fading and shadowing a single user can not make a reliable decision and collaboration of and among users is required. In this paper, it has been demonstrated that for improved detection performance decision fusion algorithm for collaborative spectrum sensing must have information about channel and the mean SNR of all secondary users. Using Monte Carlo simulations it is concluded that for optimum performance it is not necessary that all users collaborate with each other.

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Section: Collaborative Spectrum Sensing Techniquesmentioning

confidence: 99%

“…Decisions or data from the secondary users fused at the band manager to make a final decision [18]. The effects of different mean SNR on detection performance are studied under AWGN channel conditions in [19].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Spectrum Sensing for Cognitive Radio

Arshad

Moessner

2009

2009 IEEE International Conference on Communications Workshops

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“…Some hard combination algorithms are considered, such as "AND" rule, "OR" rule and "K out of N" rule [4]. However, the above algorithms seem to be too simple to meet the demand of performance.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Asynchronous Cooperative Spectrum Sensing Based on Dempster-Shafer Theory

Liu

Long

2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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In cognitive radio (CR) networks, the cooperative spectrum sensing can greatly improve the sensing performance. However, several existing cooperative spectrum sensing methods have time asynchronization assumption, which will inevitably bring the waste of waiting time, and will cause many limitations in the practical application. In this paper, we propose an enhanced asynchronous cooperative spectrum sensing framework based on the Dempster-Shafer (D-S) theory. Within such a framework, each SU calculates the trust functions with the double threshold spectrum sensing method, which improves the reliability of the local sensing results. In fusion center (FC), it uses the sliding-window method to ensure the real-time performance and the asynchronism of the fusion data. In addition, to reduce the amount of data fusion in FC, we propose a node selection algorithm using the correlations of trust functions. Our analysis and simulation results show that this method can reduce the number of sensing nodes remarkably and improve the spectrum sensing efficiency significantly.978-1-4244-9268-8/11/$26.00 ©2011 IEEE This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE Globecom 2011 proceedings.

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“…We consider a hard fusion scheme due to its improved energy and bandwidth efficiency. Among them, the OR and AND rules have been studied extensively in literature [40][41][42], since these rules are easily implementable by simple logics. And [42] shows that the OR rule outperforms the AND rule in terms of energy efficiency; thus, we employ the OR rule to combine the local binary decisions sent to the FC which makes a final decision denoted by fc .…”

Section: Global Spectrum Sensing Modelmentioning

confidence: 99%

“…Such a network can now be tested for effects with the logical OR rule [41]. The parameters are constant including the probability of spectrum sensing and SNR in the sampling time 0 for each sensor node.…”

Section: The Global Probabilities Of Spectrum Sensing and Falsementioning

confidence: 99%

A Multiobjective Evolutionary Algorithm for Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Sensor Network

Liu

Qin

et al. 2015

International Journal of Distributed Sensor Networks

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Cognitive radio has emerged as a promising solution to address the problems posed by coming spectrum scarcity for the inherently resource-constrained sensor networks. Reliability and energy consumption are key objectives for spectrum sensing in cognitive sensor networks. In this paper, a fast differential evolution algorithm is proposed to optimize the energy consumption and spectrum sensing performance jointly. By constructing a comprehensive performance metric, the joint optimization is transferred to a multiobjective optimal problem, in which the sleeping schedule and censoring mechanism are taken into consideration. The main objective of the proposed algorithm is to minimize the network energy consumption subjected to constraints on the detection performance by optimally deriving the censoring and sleeping probabilities. To accelerate the convergence speed and maintain the diversity, the algorithm utilizes the advantages of opposite-based learning for generating the initial population and a tournament scheme in mutation step. In the crossover step, a control parameters dynamic adjustment scheme is applied to make a trade-off between exploration and exploitation. Finally, a selection mechanism is introduced for generating a well-distributed Pareto optimal front. The simulation results show that the proposed algorithm can reduce the average energy consumption of cognitive sensor node, while improving the global probability of spectrum sensing.

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Data and decision fusion for distributed spectrum sensing in cognitive radio networks

Cited by 22 publications

References 4 publications

Collaborative Spectrum Sensing for Cognitive Radio

Collaborative Spectrum Sensing for Cognitive Radio

Enhanced Asynchronous Cooperative Spectrum Sensing Based on Dempster-Shafer Theory

A Multiobjective Evolutionary Algorithm for Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Sensor Network

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