Abstract
This work proposes a Spectrum Sensing (SS) scheme based on a Convolutional Autoencoder (CAE) for application in Cognitive Radio Networks. The channel occupancy is modeled as an anomaly detection problem. The CAE is trained only with noise-signal samples so that any random modulated signal observed in the wireless channel will be regarded as an outlier. The relationship between the detection threshold and the system performance is evaluated. Experiments demonstrate that the SS proposal can identify primary signal presence with better accuracy (high detection and low false alarm rate) than conventional Energy Detection.
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