Abstract
Spectrum sensing is a critical and challenging issue in cognitive radio networks. Prior research has demonstrated that using spatial diversity or temporal diversity can lead to more efficient spectrum sensing. Based on Neyman-Pearson criterion, this paper derives a novel spectrum sensing algorithm which exploits spatial diversity among multiple cognitive sensors and temporal diversity among consecutive time slots jointly. The numerical results not only verify the improvement of the sensing performance comparing with singular (spatial or temporal) diversity is applied, but also show the reduction of sensing overhead in low signal-noise-ratio (SNR) regime.
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