# A Review of Research on Spectrum Sensing Based on Deep Learning
## 4 Deep-Learning-Based Cooperative Spectrum Sensing
### Applications of Deep Reinforcement Learning to Cooperative Spectrum Sensing
Simulation experiments show that the proposed method has a 26.58 percent higher probability of PU detection than the existing CSS method. However, there are various types of attacks on the system, and related research will continue in the future. Multi-agent deep reinforcement learning (MADRL) is a method that combines deep reinforcement learning with multi-agent systems. Due to its excellent cooperative decision-making and distributed learning capabilities, several scholars have applied it to CSS. [PERSON] et al. proposed [110] realizing cooperative spectrum sensing using DQN-based deep reinforcement learning with multiple agents. The rewarding performance is obtained by a variant algorithm, UCB-H, based on the upper confidence bound (UCB) algorithm with faster search. Their experiments compare the performance of traditional algorithms based on Q-learning and \(E\)-greedy, and numerical results show that the convergence speed of the model proposed in this paper is better than the former under the same conditions. We can gain a better understanding of this topic by studying and synthesizing recent research by scholars in this direction. We summarize the above work in Table 8. It is common for researchers to employ deep Q-networks or a combination of Q-learning for cooperative spectrum sensing, where a system can make decisions more accurately in a dynamic environment by combining some corresponding algorithms. The scope of spectrum sensing has been slightly reduced due to various malicious activities or unidentified obstructions. Despite the need to account for model complexity, the need for training data, and the need for computational resources in practical scenarios, deep learning offers a promising avenue for cooperative spectrum sensing that can enhance system performance in multiple ways. More research is needed in this area to provide a boost for practical applications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Year** & **Module** & **Performance** & **Application Scenario** \\ \hline [PERSON] et al. [95] & 2019 & CNN-DCS & \begin{tabular}{c} DCS (HD): Pd = 95\% \\ (Pf = 0.5): DCS (SD): \\ Pd = 95.2\% (Pf = 0.5) \\ \end{tabular} & \begin{tabular}{c} Under harsh sensing \\ conditions, CSS with \\ correlated individual \\ spectrum sensing. \\ \end{tabular} \\ \hline [PERSON] et al. [96] & 2020 & CSS-CNN & \begin{tabular}{c} Pd = 90\% (\(-\)16 dB, 20 SUs, \\ Pf = 0.01) \\ \end{tabular} & \begin{tabular}{c} Distributed secondary \\ users accept perceptual \\ samples in severe channel \\ fading and \\ shadowing environments. \\ \end{tabular} \\ \hline [PERSON] et al. [97] & 2020 & CNN & \begin{tabular}{c} Test accuracy: 98.34\% \\ (scenario 3); 100\% \\ (scenario 2) \\ \end{tabular} & \begin{tabular}{c} Spectrum sensing \\ performance analysis in \\ dynamic scenarios with \\ different noise floors \\ and spaces. \\ \end{tabular} \\ \hline [PERSON] et al. [98] & 2019 & EL+semi-soft FC & \begin{tabular}{c} Pd = 96\% (\(-\)18 dB, \\ 60 SUs) \\ \end{tabular} & \begin{tabular}{c} CSS for cognitive radio \\ systems under \\ OFDM-based signal. \\ \end{tabular} \\ \hline [PERSON] et al. [99] & 2022 & ResNet & \
\begin{tabular}{c} Accuracy = 92\% (NSD: \\ \(-\)114 dBm \(-\)174 dBm, \\ 5 SUs) \\ \end{tabular} &
\begin{tabular}{c} Dynamic displacement of \\ the user within a given \\ area over a time period. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 8: A review of cooperative spectrum sensing based on deep neural networks and deep reinforcement learning.