Abstract
Cognitive radio (CR) has become an interesting research field that attracts researchers due to its increasing spectrum efficiency. Therefore, spectrum sensing (SS) is the essential function of cognitive radio systems. This paper presents an efficient SS model based on convolutional neural networks (CNNs). We use the spectrogram images of the received signals as the input to the CNN and use various images for signal and noise at different low primary-user (PU) signal-to-noise ratios (SNRs) to train the network model. The model extracts the main features from the spectrogram images to represent signals and noise. Hence, this model can efficiently discriminate between signal and noise at different SNRs. The detection performance of the suggested model is compared with those of the traditional one-stage, two-stage SS methods, and different previous CNN models. The obtained outcomes demonstrate that the suggested model increases the detection accuracy more than those of the previous one-stage SS methods by 17% at low SNRs of -20 dB and more than the previous two-stage SS methods by 8% at low SNRs of -20 dB. In addition, it is demonstrated that the suggested model offers shorter sensing times than those of the two-stage and one-stage SS methods in the orders of 16.3, 16.6, 1.1, and 1.5 ms at SNRs of -20, -15, -10 and -5 dB, respectively. Furthermore, the proposed model improves the detection accuracy better than the different previously compared CNN models by 28% and 19% at low SNRs of -20 and -15 dB, respectively.