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This document proposes using a CNN architecture combined with a filter bank method for spectrum sensing in cognitive radio. It summarizes existing spectrum sensing algorithms like energy detection and matched filter detection. It then describes applying a deep learning approach using a neural network with convolutional and pooling layers to classify modulation schemes like BPSK, QAM, and QPSK from received signals. The network is trained on IQ samples from a dataset captured in the Ka band and implemented i

Author

Licence Spécialisée Idbd Ensak

Language

EN