Performance Comparison of Feature-Based Detectors for Spectrum Sensing in the Presence of Primary User Traffic

The performances of four commonly used feature-based detectors for spectrum sensing are compared by assuming that the primary user may arrive or depart during the sensing period, a realistic case when the primary user traffic is high or the sensing period is long. Numerical results show that the frequent state transition of the licensed channel degrades the detector performances and that the amount of degradation depends on the primary user traffic intensity, the number of samples used, as well as the primary user signal-to-noise ratio. Also, our numerical examples show that different feature-based detectors have different robustness to the primary user traffic, allowing us to choose the best feature-based detector in terms of the primary user traffic.