# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods
### Application of Convolutional Neural Networks to Spectrum Sensing
#### 3.1.2 Spectrum-Sensing Method Based on Convolutional Neural Network
Moreover, using the spectrogram as an input to the CNN, in order to generate high-fidelity data for augmenting the training samples, [PERSON] et al. [55] proposed a spectrum-sensing convolutional neural network (S-CNN), which is innovative in the sense that the advanced deep convolutional generative adversarial network (DCGAN) is introduced for the first time in the proposed perceptual system. The spectrograms are first obtained by a short-time Fourier transform, then additional data are generated using DCGAN's data augmentation technique, and finally, the total dataset is used to train the S-CNN. Since the proposed model is a data-driven model, an efficient threshold-based detection mechanism is designed for online testing, while the former is used for offline testing. The experiments compare the performance of the CNN model without data augmentation with the DCGAN-based S-CNN proposed in this paper, and the results show that the accuracy of the model in this paper has a 10% accuracy improvement over the first model, and the detection of the generated signals, such as BPSK, QPSK, and 8 PSK, shows that there is essentially no difference in the performance, which suggests that the model is insensitive to the modulation order of the modulated signal. 2. A CNN-based spectrum-sensing method with statistical features of signals  
For the powerful feature extraction learning ability of the CNN, scholars commonly use various statistical features of received signals as inputs to the CNN for network training, and the trained network is tested for sensing of data test sets. This paper will introduce CNN-based spectrum perception methods based on various statistical features in the next subsection. 1. Covariance matrix  
The field of signal processing primarily uses covariance matrices to describe correlations and variances between different dimensions or different time points in a signal. The covariance matrix reflects crucial information about the second-order statistics of the signal, acting as a statistical property of the signal itself. It serves as a statistical property of the signal and provides crucial information about the second-order statistics of the signal. Currently, it has been found that, in general, the covariance matrix of a signal sample can be used for spectral sensing with better detection performance than several conventional methods in the presence of a low SNR and noise uncertainties. Moreover, a CNN has a strong ability to extract the features of matrix data; therefore, CNN-based covariance matrix spectrum-sensing methods are also being researched. In 2019, [PERSON] et al. introduced a data-driven spectrum-sensing method based on the covariance matrix in their published paper [56]. The proposed method utilizes the sample covariance matrix as input for a CNN to obtain test statistics. In the offline training phase, a threshold-based detection mechanism is designed, and the cost function for training the spectrum sensing model is derived using the maximum a posteriori (MAP) criterion. This case allows for the optimization of the test statistics. The experimental results demonstrate a significant performance improvement in terms of detection probability compared to several additional methods, such as energy detection (ED) and block coordinate energy detection (BCED), across the detection probability range of \(10^{-3}\) to 100. The proposed method achieves comparable performance to state-of-the-art E-C detection methods. [PERSON] et al. [57] also use the covariance matrix of the samples as a test statistic and a CNN to extract key features. In order to further improve the performance, the classifier is trained as the input of a support vector machine (SVM) in the fully connected layer,which is one of the innovations in this paper. The specific operation is to replace the loss function softmax in the original CNN with an SVM, and then use the feature vectors obtained from the covariance matrix of the CNN as training data for the SVM to obtain the classifier.