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Spectrum sensing is a critical prerequisite in envisioned applications of wireless cognitive sensor networks which promise to resolve the perceived bandwidth scarcity versus under-utilization dilemma. The Kernel method is a very powerful tool in machine learning. The trick of kernel has been effectively and extensively applied in many areas of machine learning. In this paper, we propose a novel spectrum...
Aiming to the problem of weak primary user signal detection rate in low signal-to-noise ratio environments, we propose a novel spectrum sensing method based on the principal component analysis (PCA) and random forest (RF). From the received radio signal, a set of cyclic spectrum features are first calculated, and the PCA is applied to extract the most discriminate feature vector for classification...
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