A Novel Sequential Spectrum Sensing Method in Cognitive Radio Using Suprathreshold Stochastic Resonance

As spectrum sensing detects the presence of a primary user (PU) signal, an efficient and reliable spectrum sensing scheme plays a critical role in cognitive radio (CR). For this purpose, a sequential sensing technique is introduced to reduce the sensing time to its minimum value while maintaining the desired detection performance. However, the sensing time could be still unacceptably long due to the weak PU signal, particularly in non-Gaussian noise. To improve the 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 the SSR-based sequential sensing scheme. In the scheme, the average sample number (ASN) is reduced in a single sensing node. The simulation results show that the proposed scheme significantly outperforms the conventional scheme, particularly in a low signal-to-noise (SNR) scenario.