Abstract: Spectrum sensing is a key operation in Cognitive Radio (CR) systems, where secondary users (SUs) are able to exploitspectrum opportunities by first detecting the presence of primary users (PUs). In a CR network composed of severalSUs, the detection accuracy can be much improved by cooperative spectrum sensing (CSS) strategies, which exploit thespatial diversity among SUs. However, cooperative detection strategies, which are typically based on energy sensing, donot perform satisfactorily under impairments such as non-Gaussian noise or interferences. In this paper, we propose ascheme based on kernel canonical correlation analysis (KCCA), which is able to operate in non-ideal scenarios and in atotally blind fashion. This technique is performed at the fusion center (FC) by exploiting the non-linear correlation amongthe received signals of each SU. In this manner, statistical tests are extracted, allowing the SUs to make decisions eitherautonomously at each SU or cooperatively at the FC. The performance of the KCCA-based detector is evaluated by meansof simulations and over-the-air experiments using a CR testbed composed of several Universal Radio Peripheral (USRP)nodes. Both the simulations and the measurements show that the KCCA-based detector is able to obtain a significantgain over a conventional energy detector, whose sensing performance is severely degraded by the presence of externalinterferers.

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