Moment-Based Spectrum Sensing Under Generalized Noise Channels

A new spectrum sensing detector is proposed and analytically studied, when it operates under generalized noise channels. Particularly, the McLeish distribution used to model underlying noise, which suitable for both non-Gaussian (impulsive) as well classical Gaussian modeling. The introduced adopts a moment-based approach, whereas not required know transmit signal channel fading statistics (i.e., blind detection). Important performance metrics are presented in closed forms, such false-alarm probability, detection probability decision threshold. Analytical simulation results cross-compared validating accuracy of approach. Finally, demonstrated that approach outperforms conventional energy practical case uncertainty, yet introducing comparable computational complexity.