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Weighted-clustering cooperative spectrum sensing algorithm

Authors: null kaizhi Peng; null zhongying Liu; null Lai Tu;

Weighted-clustering cooperative spectrum sensing algorithm

Abstract

Cooperative spectrum sensing has been widely used to detect the primary user with a high agility and accuracy. However, the limitation of control channel bandwidth is a challenge of cooperative spectrum sensing when the number of cognitive users becomes very large. In this paper, weighted-clustering algorithm is proposed to avoid the congestion on the control channel and lessen the sensing delay. Multi-threshold mechanism is used to reduce transmission overhead and improve the sensing performance. Furthermore, algorithm flow and algorithm exception processing are both studied and the analytical performance results are given. Numerical results show that the sensing performance is improved significantly as opposed to conventional sensing algorithm.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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