# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods
### Application of Long Short-Term Memory Networks to Spectrum Sensing  
#### 3.2.1 LSTM  
Recurrent neural networks (RNNs) are another commonly used form of deep neural network used in the field of spectrum sensing; they are more  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Input** & **Model** & \begin{tabular}{c} **Detection** \\ **Probability** \\ \end{tabular} & **Advantage** \\ \hline [PERSON] et al. [65] & 2021 & ES feature & ResNet & 90.5\% (\(-10\) dB) & \begin{tabular}{c} Comparing the other models with the same feature inputs, ResNet has \\ the best performance. \\ \end{tabular} \\ \hline [PERSON]. [PERSON] et al. [69] & 2022 & \begin{tabular}{c} Spatio-temporal feature \\ \end{tabular} & NN-ResNet & 90\% (\(-11\) dB) & \begin{tabular}{c} Ability to make \\ better predictions with fewer sensors \\ and lower \\ error rates. \\ \end{tabular} \\ \hline [PERSON] et al. [72] & 2022 & \begin{tabular}{c} Two-dimensional time–frequency matrix \\ \end{tabular} & STFT-ImpResNet & 94\% (\(-19\) dB, noise power = \(1.5\) dB) & \begin{tabular}{c} The robustness \\ and detection probability of this \\ model is better \\ than STFT-CNN, \\ SVM, etc. \\ \end{tabular} \\ \hline [PERSON] et al. [73] & 2022 & \
\begin{tabular}{c} Time–frequency matrix obtained after signal processing by wavelet variation \\ \end{tabular} & WT-ResNet & 91\% (\(-14\) dB) &
\begin{tabular}{c} Improved \\ detection of \\ non-stationary signals in low \\ signal-to-noise ratio environments. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: A review of residual-neural-network-based spectrum-sensing algorithms.  
series signals [75]. In general, an RNN can provide a better channel selection strategy for cognitive radio by exploiting historical information to predict the future utilization of a certain band. Alternatively, it can help to detect and identify potential sources of interference by analyzing time series in the spectrum.  
In practice, spectrum sensing is also critical for tasks that require processing lengthy sequences with long-term dependencies and learning from complex and noisy spectral data, where LSTMs are commonly used more regularly than RNNs.  
Long short-term memory (LSTM) is a special type of recurrent neural network [76]. The relationship between the internal variables of the LSTM cell is shown in Figure 13. The memory cell of LSTM mainly consists of three types of gates, namely, the input gate, forget gate, and output gate, to solve the problems of gradient vanishing and gradient explosion in traditional RNNs. The control of information transfer in the model is performed by the cell state of the cell. In the model, these gates govern the control of information transfer through the utilization of sigmoid activation functions.  
Due to its excellent ability to model long-term dependencies, LSTM is more sensitive to the long-term relationships between spectrum signals and is, therefore, able to better understand and predict the dynamics of the spectrum. On the other hand, the LSTM is able to memorize the previously observed state information and combine it with the current observation information for prediction and decision making, thus completing the sensing of the spectrum state.  
Broadly speaking, the research on LSTM in spectrum perception is mainly divided into two categories: one is to extract various features of the original signal data for training and learning to complete spectrum perception; and the other is to optimize the structure of the model or combine it with other suitable modules and algorithms to make the model better solve the problems encountered in spectrum perception, such as unstable perception accuracy in dynamic environments and low detection probability at low SNRs, etc. In the following, we summarize the current status of research ideas in both categories.