# A Review of Research on Spectrum Sensing Based on Deep Learning
## 6 Challenges and Perspectives  
There is a growing demand for efficient utilization of spectrum resources in communications, and novel techniques such as deep learning are being applied in spectrum sensing to improve sensing performance, which has received increasing attention from scholars. As the main body of deep learning, as well as different algorithms that can optimize the network model, scholars design different network structures and combine reasonable algorithms to improve the sensing performance of the model, but there are difficulties and challenges such as data dependence, real-time applications, and security. Therefore, deep-learning-based spectrum sensing requires a certain amount of research time and technical support from scholars before it can be truly applied in practical, complex sensing environments to improve the efficiency of spectrum utilization, which can have a positive impact on the communication domain. Based on the current state of research, key future research directions for deep-learning-based spectrum sensing include the following.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Spectrum Sensing** & **References** & **Method/Model** & **Performance** & **Application Scenario** \\ \hline \multirow{8}{*}{Based on conventional methods} & \multirow{8}{*}{[34; 35; 36]} & \multirow{8}{*}{Matched filter detection} & Better performance than energy detection in ideal environments. & Knowing a priori information about the signal, higher SNR, and higher phase synchronization. \\ \cline{3-5}  & & & Eliminates noisy signals and interference from malicious users for better spectrum monitoring and sharing than single-node & The environment allows for effective linkage of sensory information from multiple nodes or devices. \\ \cline{3-5}  & & & & The environment suffers from channel multipath fading, shadowing effects, and receiver instabilities. \\ \hline \multirow{8}{*}{Based on conventional methods} & \multirow{8}{*}{[37; 38; 39; 40; 41; 42; 43]} & \multirow{8}{*}{Cooperative spectrum sensing} & By extracting higher-order statistical features of the signal, it is possible to achieve higher detection probabilities at low SNRs, and the model is also more robust. & Do not need a priori information about the signal and want to improve the detection probability and robustness at low SNRs. \\ \cline{3-5}  & & & & Want to further increase the depth of the CNN, thus achieving better performance and avoiding the problem of gradient drop in DCNNs. \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & & \\ \hline \multirow{8}{*}{Based on deep learning} & \multirow{8}{*}{[77; 78; 79; 80; 81; 83]} & \multirow{8}{*}{LSTM} & Higher accuracy can be achieved at a low SNR through efficient learning of raw signal data or correlation information between signals. & The need to learn complex and noisy spectrum data for tasks that require long sequences with long-term dependencies. \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \hline \multirow{8}{*}{[104; 106; 108; 109; 110]} & \multirow{8}{*}{DQN} & It allows the system to make more accurate decisions in dynamic environments and improves the robustness and accuracy of sensing models. & Looking to address distributed decision making, synchronization issues, security, and robustness in CSS, or to improve sensing accuracy in multi-user and dynamic scenarios. \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \hline \end{tabular}
\end{table}
Table 9: _Cont._