Lowering the signal-to-noise ratio wall for energy detection using parameter-induced stochastic resonator

Energy detection is the most frequently used spectrum sensing technique, however, in uncertain low signal-to-noise ratio (SNR) conditions, it generally suffers from the `SNR wall´, that is, a minimum SNR below which it is impossible to reliably detect a signal. To address this issue, an improved energy detection (IED) algorithm based on non-linear parameter-induced stochastic resonator (PSR) is proposed in this study. By adopting the method which combines classic adiabatic approximation stochastic resonance (SR) theory and non-classic parameter-induced SR theory, an analytical expression of SR system parameters is derived. On this basis, a PSR is proposed which is mathematically testified to have the ability to improve the SNR of the received signal. Further, an IED algorithm is proposed by introducing the PSR as the preprocessor of energy detection. Theoretical analyses and simulation results prove that a significantly detection performance and SNR wall improvement can be achieved using the proposed IED algorithm.