Cyclostationarity-based soft cooperative spectrum sensing for cognitive radio networks

Abstract

Reliable detection of primary users (PUs) in the presence of interference and noise is a crucial problem in cognitive radio networks. To address the above issue, cooperative spectrum sensing based on cyclostationary feature detection that can robustly detect weak primary signals has been proposed in the literature. Among different cooperative techniques, in this study the authors focus on combination of soft decisions derived based on asymptotic properties of cyclic autocorrelation estimates. The objective is to maximise deflection coefficient performance metric at the fusion centre, where linear combination of cyclostationary soft decisions is performed. Since the proposed method allows for distributed cyclostationarity spectrum sensing, it is more reliable and faster than non-cooperative traditional multi-cycle cyclostationarity detection schemes. To reduce the computational complexity of the exact distribution of proposed test statistic at fusion centre, the authors derive efficient approximations for the distribution under null and alternative hypotheses. Extensive simulation results in different scenarios demonstrate the advantage of the proposed method and confirm the analytic performance characterisations. In addition, the authors study the impact of mobility of cognitive devices on the cyclostationarity of received signals and verify our analysis via simulation.