# A Review of Research on Spectrum Sensing Based on Deep Learning
## 4 Deep-Learning-Based Cooperative Spectrum Sensing
### Applications of Deep Neural Networks in Cooperative Spectrum Sensing  
Understanding the pertinent literature from recent years has revealed that ResNet, graph neural networks, and deep neural networks are the most frequently used CNNs in cooperative spectrum sensing. The next section will summarize the research findings of academics in cooperative spectrum sensing from recent years. In 2019, [PERSON] et al. considered the case where multiple SUs share sensing information to make state judgments on whether a single PU occupies more than one frequency band. They proposed a CNN-based CSS model in [95] to improve the sensing performance and stability by considering the state and spectral correlation of the channel and extracting the data space features to be input into the optimized modest-sized CNN. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Input** & **Model** & **Performances** & **Advantage** \\ \hline \multirow{8}{*}{[PERSON] et al. [92]} & \multirow{8}{*}{2023} & Features such as & & & The model can effectively solve \\  & & & & & the domain \\  & & & correlation, texture, & & & \\  & & shape, and power & & (Recall = 0.4, & adaptive problem \\  & & & in spectrograms & & MAP = 95.04) & and improve the \\  & & & & MAP = 95.04) & migration learning \\  & & & & & to further enhance \\  & & & & & the generalization ability. \\ \hline \multirow{4}{*}{[PERSON] et al. [94]} & \multirow{4}{*}{2023} & Characteristics of & & Recovery & The proposed \\  & & data distribution in & & Probability of & model performs \\  & & different time & & Support Set = 0.91 & well with very low \\ \cline{1-1}  & & periods & & (\(-\)8 dB) & SNRs and fewer \\ \cline{1-1} \cline{2-5}  & & & & & sampled channels. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._The simulation results show that the method outperforms the traditional CSS methods such as K-out-of-N and SVM. The paper is also the first to apply deep neural networks to CSS. In Ref. [96], [PERSON] et al. applied it to distributed cooperative spectrum sensing by using the sample covariance matrix as a test statistic as an input to the CNN, i.e., collecting sensory information from different SUs for learning. The proposed model is CSS-CNN, and the experiments are performed under channel fading to compare with other CNN and CLDNN methods, which perform adequately at low SNRs. Whereas in [97], [PERSON] et al. used a CNN in a centralized cooperative spectrum-sensing framework and considered spatio-temporal data, channel shadowing, and spatial correlation, and the CNN model was also trained for decision making through data fusion centers. The results show that the CNN-based framework maintains strong sensing accuracy under noisy conditions. Although the performance and complexity of CNN can be affected by noise, the results are still accurate due to the decision inputs from historical training and local user measurements. [PERSON] [98] focused his research on the stacking approach of fusion centers in CSS and perceived OFDM signals in CSS. Recurrent correlated feature learning is fed to a CNN, and a training database is built through a bagging strategy. The innovation is that the data fusion center with the stacked generalization approach can better learn the probabilistic prediction of PU states. The experimental results show the advantage over the traditional CSS method by showing both the detection probability and the false alarm probability. The application of ResNet to cooperative spectrum sensing is also currently in progress. The authors [PERSON] et al. consider experiments with neural network models placed in a CSS environment in a scenario where the user maintains a dynamic movement within that range. In Ref. [99], they proposed a cooperative spectrum-sensing approach based on ResNet. The 3D matrix is used as the input to ResNet, conv3D is used for feature extraction, and two additional conventional CNN and RNN models are used for experimental comparison.