# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods  
Deep learning, as a fresh development direction of machine learning, made its rise in 2012 when deep learning in the ImageNet competition [45] achieved the best results. Immediately following, in 2016, the artificial intelligence AlphaGo defeated the world champion [PERSON] [46] in the world Go competition, which inspired another round of investment in deep learning and the third wave of artificial intelligence. On 30 November 2022, ChatGPT [47], a large language model based on the Transformer neural network architecture released by OpenAI, began to seriously ignite the attention and discussion of people all over the world about AI and the deep learning technology behind it.  
Deep learning is not only fully developed in the fields of large language modeling and computer vision, but also in the field of communication. There are more and more scholars conducting in-depth research on it [48]. In the communication domain, deep learning has been applied mainly in the branches of channel estimation, signal detection, communication system optimization, and cognitive radio.  
For example, spectrum sensing in cognitive radio can be used to optimize performance using deep learning techniques, and deep-learning-based spectrum sensing has higher accuracy and robustness than traditional spectrum-sensing methods. Essentially, deep-learning-based spectrum sensing is a sensing method that is known to extract statistical properties or higher-order statistical properties of the received signal to learn classification. A deep neural network learns by feeding features of a particular signal, and the trained neural network is able to perform perceptual recognition without prior information about the received signal. The deep-learning-based spectrum-sensing model is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Spectrum Sensing** & **Method** & **Applicable Scenario** \\ \hline \multirow{6}{*}{Single-node spectrum sensing} & \multirow{3}{*}{Energy detection} & When the noise level in the system is known or can be estimated, or when the statistical information about the signal is not known. \\  & & & \\ \cline{2-3}  & & \multicolumn{2}{c}{Suitable for detecting cyclical signals,} \\  & & such as modulated signals and \\  & Cyclostationary feature detection & communication signals, as well as \\  & & spectrum sensing in complex \\  & & channel environments. \\ \cline{2-3}  & Matched filter detection & Known master user signal information. \\  & & High-phase synchronization. \\ \hline \multirow{6}{*}{Cooperative spectrum sensing} & \multirow{3}{*}{Centralized cooperative spectrum sensing} & Relatively stable network structure and abundant device computing resources. \\  & & There is a need for unified decision \\  & & making based on sensed data. Scenarios \\  & & with high requirements for security and \\  & & monitoring of spectrum usage [38]. \\ \cline{2-3}  & \multirow{3}{*}{Distributed cooperative spectrum detection} & Local information can be shared between \\  & & cognitive nodes relatively frequently and \\  & & fluently. Control channel resources are \\  & & relatively abundant. Want to improve \\  & & fault tolerance [44]. \\ \cline{2-3}  & \multirow{3}{*}{Relay-assisted cooperative spectrum detection} & Environments where certain nodes \\ \cline{1-1}  & & cannot communicate directly with the \\ \cline{1-1}  & & fusion center or other nodes due to \\ \cline{1-1}  & & geographic location or other factors [44]. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of conventional spectrum-sensing methods.  
The current research on deep learning in spectrum sensing mainly focuses on several popular and high-performing networks, and the next section focuses on the application of several typical deep learning neural networks in spectrum sensing, such as CNNs, ResNet, LSTM, and other deep learning neural networks.