# 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
The experimental results show that as the number of training times of the proposed model network is gradually increased to about 45, the detection probability of the spectrum-sensing model is stabilized at 96%, and the performance of the false alarm probability is fixed. Compared with the separate CNN and SVM algorithms, there is a performance enhancement in the detection at a low SNR. Most of the recent research in this area has been on single-band spectrum sensing. In the real world, however, many situations involve multiple antenna sub-users sensing several bands in a row that are occupied by multiple PUs and need higher opportunity throughput to achieve dynamic spectrum access. To solve this problem, [PERSON] et al. proposed a multi-band spectrum-sensing method based on a CNN with a covariance matrix (MJCM-CNN) in [58]. The difference compared to the single-band spectrum sensing in [57] is that this method needs to consider the correlation information between each sub-band, so the feature extraction of the covariance matrix of the multiband by the CNN can effectively learn the correlation information between each sub-band. Since spectrum sensing for each sub-band individually leads to under-utilization of the information between two consecutive frequency bands, this paper achieves joint multi-band spectrum sensing by connecting all covariance matrices into the frequency band covariance matrix array R. The experimental results show that the performance of the MJCM-CNN proposed in this paper is significantly better than the CM-CNN method mentioned in the previous literature [56] in the range of \(-20\) dB \(-16\) dB SNR, and it is significantly ahead of the traditional spectrum-sensing methods. Moreover, [PERSON] et al. utilize the method in their 2023 article [59]. Their main focus is on the application of deep learning for spectrum sensing in connected vehicle communications. The objective of their study is to enhance spectrum utilization for connected vehicle communications. Unlike all of the above methods, this article uses kernel principal component analysis (KPCA) nonlinear mapping to extract feature vectors from the sampled signal covariance matrix, mapping the raw data to a higher-dimensional data space in order to analyze the sampled signal data. This case has the advantage of large immunity to noise in complex and variable communication environments, where the covariance matrix is constructed, and then singular value decomposition is performed to obtain the feature vector that distinguishes the sampled signal from the noise. Experiments with modulated signals such as 2 FSK, QPSK, etc., at different SNRs are compared with methods based on SVM, LeNet5, etc., and it is shown that the proposed algorithm achieves 100% detection probability when the SNR is above \(-3\) dB. By Fourier transforming a signal's autocorrelation function, one can obtain the spectral correlation function (SCF), also referred to as spectral correlation density. It is commonly used in the modern signal processing community to describe the spectral properties of periodic statistical signals, and in recent years, some scholars have combined SCF with CNN for spectrum sensing. In 2021, [PERSON] et al. proposed a CNN-based spectrum sensing model utilizing the spectrum correlation function in their paper [60]. The SCF is used as input to the proposed CNN model. To enable signal recognition and spectrum-sensing experiments in two different scenarios, the first scenario involves training a CNN for signal classification by extracting cyclic spectra of GSM, UMTS, LTE, and the spatial spectrum. In the second scenario, the cyclic spectra of relevant signals were input into a CNN detector to detect the presence of primary user (PU) signals. If a PU signal is detected, the next-level CNN classifier is activated to determine the type of PU signal, enabling spectrum sensing and signal recognition. The experimental scope of this model is in the range of cellular signals and is applicable to any signal exhibiting cyclostationarity.