# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods
### Other Neural Networks in Spectrum Sensing  
In addition to convolutional neural networks and long short-term memory networks, scholars are investigating the use of additional deep neural network applications in the field of spectrum sensing, which are currently achieving some results; for example, artificial neural networks (ANNs) [85], deep neural networks (DNNs), and generative adversarial networks (GANs), etc., An ANN is a network that mimics the working mechanism of neurons in the human brain, has strong adaptive learning capabilities, and is a fundamental network for deep learning. It mainly consists of three network structures, input, hidden, and output layers, and the current CNNs, RNNs, GNNs, etc., are variants of ANNs. Numerous academics have applied ANNs in the field of spectrum sensing in recent years with encouraging results due to its high fault tolerance and strong adaptive learning ability. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Author** & **Year** & **Input** & **Model** & **Performances** & **Advantage** \\ \hline \multirow{4}{*}{[PERSON] et al. [77]} & \multirow{4}{*}{2019} & \multicolumn{3}{c}{The previous sensing event and present sensing event} & \multirow{4}{*}{LSTM} & \multirow{4}{*}{Pd = 91\% (\(-\)10 dB)} & Ability to improve detection probability and classification accuracy in low signal-to-noise ratio environments. \\ \cline{5-6}  & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [78]} & \multirow{4}{*}{2020} & \multirow{4}{*}{Temporal correlation} & \multirow{4}{*}{LSTM-SS} & \multirow{4}{*}{Pd = 99\% (\(-\)10 dB)} & Innovative proposal of PAS-SS to improve detection probability by calculating PU activity statistics. \\ \cline{5-6}  & & & & & \\ \hline \multirow{4}{*}{[PERSON] [79]} & \multirow{4}{*}{2022} & \multicolumn{3}{c}{Raw data and cyclostationary feature} & \multirow{4}{*}{LSTM} & \multirow{4}{*}{Pd = 99.4\% (20 dB)} & The model is suitable for classification by modulation type and pulse shape of communication signals. \\ \cline{5-6}  & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [80]} & \multirow{4}{*}{2022} & \multicolumn{3}{c}{Covariance matrix of the signals received by the array} & \multirow{4}{*}{CM-LSTM} & \multirow{4}{*}{Pd = 100\% (\(-\)10 dB)} & The model performs better than other existing methods in a multi-antenna environment. \\ \cline{5-6}  & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [81]} & \multirow{4}{*}{2018} & \multirow{4}{*}{Data of single spectrum point with temporal correlation} & \multirow{4}{*}{Taguchi method + LSTM} & \multirow{4}{*}{CDF = 90\% (RMSE = 15)} & Optimizing the network by introducing \\ \cline{5-6}  & & & & & \\ \cline{5-6}  & & & & (RMSE = 15) & [PERSON]’s method to reduce time consumption and computational resources. \\ \hline \multirow{4}{*}{[PERSON] et al. [83]} & \multirow{4}{*}{2023} & \multirow{4}{*}{Energy and correlation features} & \multirow{4}{*}{Cuttle Fish + LSTM} & \multirow{4}{*}{Pd = 90\% (\(-\)20 dB)} & The model achieves lower complexity and low computational overhead to other existing LSTM models. \\ \cline{5-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A review of spectrum-sensing algorithms based on long short-term memory networks. [PERSON] et al. [86] proposed a spectrum-sensing method based on wavelet transform and an ANN. The central idea of the proposed method is to remove the noise in the noise-containing signal by wavelet thresholding, then to use the recurrent smooth feature method to separate the residual noise in the target signal, and finally, ANN learns the recurrent smooth feature of the signal to complete the classification. The experimental assumption is that the PU signal is a BPSK signal with AWGN, and the tests are performed in the SNR range of \(-20\) dB to \(0\) dB. The simulation results show that the sample signal preprocessed by a wavelet transform through the ANN performs better than the single method of learning the signal features directly with the ANN.