A Log-likelihood based Cooperative Spectrum Sensing Scheme for Cognitive Radio Networks

In cognitive radio networks, cooperative spectrum sensing is used to exploit spatial diversity of secondary users, in order to reliably detect an unoccupied licensed spectrum. Since each secondary user experience different channel conditions, therefore observations of secondary users are weighted according to the reliability factor (weight) of individual secondary user. However, the weight estimation is done in each sensing interval resulting in high cooperation overhead in terms of time, processing and reporting channel bandwidth. In this paper, we propose a novel cooperative spectrum sensing scheme which is based on the Log-Likelihood ratio (LLR). The secondary users employ two threshold level to minimize false-alarms and miss-detection of the primary user's signal. In addition, unknown parameters for weight computation are estimated/updated after certain number of sensing intervals instead of estimating them in every sensing interval. In this way, the detection performance is increased while the cooperation overhead reduces. To evaluate the detection performance of the proposed scheme we do a simulation study and compare it with the popular optimal detection schemes.