CNN and DCGAN for Spectrum Sensors over Rayleigh Fading Channel

Spectrum sensing (SS) has attracted much attention in the field of Internet of things (IoT) due to its capacity of discovering the available spectrum holes and improving the spectrum efficiency. However, the limited sensing time leads to insufficient sampling data due to the tradeoff between sensing time and communication time. In this paper, deep learning (DL) is applied to SS to achieve a better balance between sensing performance and sensing complexity. More specifically, the two-dimensional dataset of the received signal is established under the various signal-to-noise ratio (SNR) conditions firstly. Then, an improved deep convolutional generative adversarial network (DCGAN) is proposed to expand the training set so as to address the issue of data shortage. Moreover, the LeNet, AlexNet, VGG-16, and the proposed CNN-1 network are trained on the expanded dataset. Finally, the false alarm probability and detection probability are obtained under the various SNR scenarios to validate the effectiveness of the proposed schemes. Simulation results state that the sensing accuracy of the proposed scheme is greatly improved.