An Adaptive Double-Threshold Spectrum Sensing Algorithm under Noise Uncertainty

Spectrum sensing is a very important technique in cognitive radio system. Matched filter detection, cyclostationary feature detection and energy detection are traditional and classical algorithms in spectrum sensing technology in cognitive radio system. Since energy detection is simple, and it does not require the priori information, energy detection based spectrum sensing has been proposed and studied widely. Collisions between the cognitive user and the primary user are sensitive and significant to detection performance. In this paper, an adaptive double-threshold spectrum sensing algorithm is proposed to solve the problem, which detection probability would be declined when signal to noise rate decreases under noise uncertainty. Theoretical analysis and simulation show that the spectrum detection performance can be improved more significantly and interference level to the primary user can be declined when noise is uncertain.