Simulation Study on Dual-Threshold Spectrum Detection Performance Based on Energy Detection

Dual-threshold spectrum detection based on energy detection is a technique widely applied in wireless communication systems. This method effectively detects signal presence through energy detection and spectrum analysis of signals. However, its performance varies under different noise environments. Therefore, this paper conducts simulation studies to investigate its behavior under various noise conditions. The simulation results demonstrate that the detection performance is significantly affected in low signal-to-noise ratio (SNR) environments, while maintaining excellent detection capability in high SNR scenarios. Consequently, practical applications require selecting appropriate dual-threshold spectrum detection schemes based on specific noise environments to ensure optimal system performance.

Implementation typically involves calculating signal energy values and comparing them against two predefined thresholds using MATLAB or Python. Key algorithms include noise variance estimation, energy computation through FFT transformation, and decision logic where signals are classified as present, absent, or requiring further analysis based on threshold comparisons. The detection probability and false alarm rate are crucial metrics evaluated through Monte Carlo simulations.