Spectrum Sensing Based on Blindly Learned Signal Feature

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Spectrum sensing is the major challenge in the Cognitive Radio (CR). The authors propose to learn local feature and use it as the prior knowledge to improve the detection performance. They define the local feature as the leading eigenvector derived from the received signal samples. A Feature Learning Algorithm (FLA) is proposed to learn the feature blindly. Then, with local feature as the prior knowledge, they propose the Feature Template Matching algorithm (FTM) for spectrum sensing.