Stochastic Resonance Noise Enhanced Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is a fundamental technology to detect the presence of primary user in cognitive radio networks. Usually, the requirements for spectrum sensing are very stringent. It requires that the spectrum sensing scheme has a good performance even in extremely low signal-to-noise ratio (SNR) environments. In this paper, we propose a novel spectrum sensing method based on stochastic resonance (SR) noise enhance detection (NED) to meet the requirement. For the proposed SR NED based spectrum sensing scheme, a specific signal-independent SR noise is added to the received signal so that the probability distribution of the detection statistics is modified to match the applied nonlinear suboptimal detector better. The performance of the applied detector can then be significantly improved according to the basic principle of SR NED. Under the constraint of the false alarm probability, the method to find the optimal SR noise is provided for both single node sensing and multiple nodes cooperative sensing by maximizing the probability of detection. For single node sensing, the popular energy detector is considered. For cooperative sensing, both maximal ratio combination (MRC) and equal gain combination (EGC) based energy detectors are investigated. Theoretical analysis and simulations results show that the proposed SR NED based spectrum sensing schemes can achieve much better performance than that of the applied original detectors.