Comparison of spectrum sensing methods for cognitive radio under low SNR

In this paper we compare several popular and top performing spectrum sensing algorithms for cognitive radio under low signal to noise ratio scenario, namely the Energy Detection, the Cyclostationary Detection, and the Eigenvalue based Detection. Especially, when auxiliary sensing slots are available, we incorporate modified hybrid algorithms that can exploit the auxiliary information into comparison. Simulation results based on QPSK primary signal are given, in the form of ROC curves, SNR gains and auxiliary gains for pre-defined false alarm probability and detection probability. These results can be useful for proper design of spectrum sensing algorithms under low SNR.