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This paper presents an analyzing on the prediction of a primary signal in cognitive radio networks using a hybrid algorithm based on two parts, know as: an alpha-beta filter and a neyman-pearson (NP) detector. Today, it is important to predict a primary signal in a cluttered environment and especially when the secondary user (SU) is moving. However, the challenges of this contribution are based on...
Cooperative spectrum sensing is a fast and effective approach to improve sensing performance in cognitive radio networks. However, in order to reduce the bandwidth demands of cooperation, censoring is needed. In this paper, we propose a Two-Round Cooperation based Spectrum Sensing algorithm (TRCSS) according to local three-threshold decision results. More specifically, after the first round censoring...
Efficient and reliable spectrum sensing plays a critical role in cognitive radio networks. This paper presents a cooperative sequential detection scheme to minimize the average sensing time that is required to reach a detection decision. In the scheme, each cognitive radio computes the Log-Likelihood ratio for its every measurement, and the base station sequentially accumulates these Log-Likelihood...
In cognitive radio systems, unlicensed users can use the frequency bands when the licensed users are not present. Hence reliable detection of available spectrum is the foundation of cognitive radio technology. In order to efficiently detect the licensed users under prior knowledge constraints in the fusion center, a novel cooperative spectrum sensing algorithm based on Dempster-Shafer theory is proposed...
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