Wavelet Transform For Spectrum Sensing In Cognitive Radio

Spectrum sensing is a critical component for Cognitive Radio technologies. Second Users (SUs) should be able to sense the presence of the Primary Users (PUs) continuously, instantly, and accurately to guarantee that when PUs return, SU evacuate the occupied spectrum immediately. In traditional spectrum sensing algorithms, certain degree of the prior knowledge such as the level of the white noises is assumed. In practice, however, such information is dynamic and its maintenance and distribution can be costly. In order to conduct the efficient and accurate spectrum sensing without any prior knowledge, in this paper we propose a novel wavelet transfom based spectrum sensing algorithm called WATRAB. The basic idea is that PU’s signals only carry a limited amount of information while the noise can be considered as having many. By carefully selecting a wavelet transform method, such a difference can be exploited to generate very different transform results. To demonstrate the effectiveness, we implement WATRAB based on USRP2platform and also conduct comprehensive simulation experiments. Experimental results show that WATRAB can significantly improve the sensing accuracy by up to14db with the same accuracy compared with the simple energy detection, and the compared with the cyclostationary method, the complexity reduced from O(n2) to O(n).