# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods
### Other Neural Networks in Spectrum Sensing
proposed a deep convolutional neural network capable of realizing time-frequency fusion that is adjustable according to different time periods [94]. The core idea is to compress the signal using MWC to obtain the spectral matrix, use the principal component analysis (PCA) algorithm to downscale the data, and use feature extraction as the input to the network. The model proposed in this paper mainly consists of two parts: the main network is made up of multiple CNNs superimposed on each other to extract features, and the auxiliary network is used to learn the distribution pattern of the data. The pre-trained model obtains the original model through migration learning. Their experimental results show that it can achieve better perceptual performance than DCS-GAN in the case of a low SNR and low sampling channels. This section describes the application of ANNs, which have been used more in the last few years, and deep neural networks such as GANs and DNNs, in the field of spectrum sensing. Moreover, these applications are summarized in Table 7, from which we can find that ANN-based methods have been extensively studied in the past few years with certain results, and now they are typically combined with better deep neural networks for learning perception. Additional deep neural networks are mainly used to perform spectrum prediction and sensing by learning specific features in the signal, which have better results and require additional research in the future. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Input** & **Model** & **Performances** & **Advantage** \\ \hline \multirow{3}{*}{[PERSON] et al. [86]} & \multirow{3}{*}{2019} & Cyclostationary features of training samples & Wavelet Transform + ANN & \multirow{3}{*}{Pd = 90\% (\(-15\) dB)} & Enhanced detection performance in low-SNR environments. \\ \cline{5-6}  & & & & & The model can improve the detection probability by learning the information provided by multiple detectors. \\ \hline \multirow{3}{*}{[PERSON] et al. [87]} & \multirow{3}{*}{2021} & Characterization between the phase-difference distributions of noise-perturbed signals and Gaussian noise & \multirow{3}{*}{PDD, BPDD} & PDD: Pd = 90\% & When the carrier frequency of the sensed signal is \\  & & & & (\(-15\) dB, Pf = 0.5, AWGN channels) & unknown, \\ \cline{5-6}  & & & & BPDD: Pd = 83\% & the proposed BPDD model \\ \cline{5-6}  & & & & AWGN & outperforms other existing schemes. \\ \hline \multirow{3}{*}{[PERSON] et al. [90]} & \multirow{3}{*}{2023} & Wideband spectrum signals & \multirow{3}{*}{ADMM + DNN} & MSE = 0.75 (\(-10\) dB) & The model accelerates convergence at lower sampling rates and is robust at low signal-to-noise ratios. \\ \cline{5-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A review of spectrum-sensing algorithms based on other deep neural networks. The next section summarizes the current state of research and results achieved by scholars on deep learning for cooperative spectrum sensing in recent years.