Performance Degradation of Static Fusion under Stochastic Resonance: Revisiting the SSFW Method

Abstract This study revisits the Static Spectrum Fusion Weighting (SSFW) method for spectrum sensing under stochastic resonance (SR) conditions. While SSFW has shown efficiency in stable noise environments, its performance degrades significantly under nonstationary noise perturbations introduced by SR. This paper proposes an extension to the SSFW method by integrating adaptive fusion weight recalibration and dynamic thresholds based on real-time noise estimates. Simulation results show that the revised SSFW method outperforms traditional fusion schemes, such as PSO and RSA, under varying levels of SR noise variance, achieving up to 20\% higher detection probability. We demonstrate the robustness of the proposed method and highlight the critical role of adaptive fusion strategies in maintaining performance in stochastic environments.