A novel sequential spectrum sensing method in cognitive radio using suprathreshold stochastic resonance

As spectrum sensing detects the presence of PU signal, an efficient and reliable spectrum sensing scheme plays a critical role in CR. For this purpose, sequential sensing technique is introduced to reduce the sensing time to the minimum while desirable detection performance is maintained. However the sensing time could still be unacceptably long due to the weak PU signal, especially in non-Gaussian noise. To improve spectrum sensing efficiency, we propose a novel sequential sensing scheme based on suprathreshold stochastic resonance (SSR). We address the theoretical bound to achieve potential performance improvement and give the applicable algorithm of SSR-based sequential sensing scheme. In the scheme, the average sample number (ASN) is reduced in a single sensing node using nonlinear stochastic resonance method. The simulation results show that the proposed scheme significantly outperforms the conventional scheme, especially in low signal-to-noise ratio (SNR) scenario.