Blind Spectrum Sensing Using Symmetry Property of Cyclic Autocorrelation Function: From Theory to Practice
Résumé
Spectrum sensing has been identi ed as the key step of the cognition cycle and the most important function for the establishment of cognitive radio. In this paper, we propose to implement and test the classical energy detection and a novel blind cyclostationnary feature detector based on the symmetry property of the cyclic autocorrelation function (CAF) on the real USRP/GNU Radio platform. Both detection algorithms are employed to blindly sense spectrum for opportunistic access. This study shows that the blind cyclostationnary feature detector outperforms classical energy detector while guarantying an acceptable complexity compared to the ED simplicity. Moreover, di erent experimental results indicate that the blind sensing detector can achieve high detection probability at a low false alarm probability under real channel conditions and low signal-to-noise ratio (SNR).