Significant cycle frequency based feature detection for cognitive radio systems

In cognitive radio systems, one of the main requirements is to detect the presence of the primary users´ transmission, especially in weak signal cases. Cyclostationary detection is always used to solve weak signal detection, however, the computational complexity prevents it from wide usage. In this paper, a significant cycle frequency based feature detection algorithm has been proposed, in which only cycle frequency with significant cyclic cumulant is considered for a certain modulation mode. The proposed algorithm greatly reduces the computation complexity for cyclic feature detection. Simulation results show that the proposed algorithm has a remarkable performance gain than energy detection when supporting fast detection with low computational complexity.