Optimal Linear Weighted Cooperative Spectrum Sensing for Clustered-based Cognitive Radio Networks

Abstract

The lack of spectrum resources restricts the development of the wireless communication-oriented applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, cognitive radio is regarded as an effective technology. Cooperative spectrum sensing with multi cognitive users can improve the low detection performance caused by channel fading or shadow effect. However, it also may lead to poor detection accuracy due to poor channel conditions of individual users. In order to solve the above problems, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.