# A Review of Research on Spectrum Sensing Based on Deep Learning
## 5 Performance Comparison of Conventional Methods and Deep-Learning-Based Spectrum Sensing  
To further exemplify the advantages of deep-learning-based spectrum-sensing methods over conventional methods in terms of performance, as well as the breakthroughs in the limitations in application scenarios, we review some of the reviews and continue the comparison by summarizing them in Table 9.  
We have summarized the conventional spectrum-sensing methods in Table 3, so we only provide a brief introduction here. In terms of applicable scenarios, single-node spectrum sensing using conventional methods is usually more limited. Although energy detection is relatively simple and easy to implement, it can only perform well when the SNR is high and cannot be used to detect spread spectrum signals and frequency hopping signals [111]. Energy detection tends to perform less well in environments with a low SNR or channel fading. Although cyclostationary feature detection performs best among the single-node spectrum-sensing methods and can operate at lower SNR, it requires partial information about the PU signal. Implementing it is more complex, resulting in long detection times and low network throughput. The work of matched filter detection is more demanding; not only must we know the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Year** & **Module** & **Performance** & **Application Scenario** \\ \hline \multirow{2}{*}{[PERSON] et al. [100]} & \multirow{2}{*}{2022} & Deep ResNet & Pd = 95.78\% (the matrix size of 10 \(\times\) 5, Rician channel) & Sensing spectrum availability with crowd sensors via DNR. \\ \hline \multirow{2}{*}{[PERSON] et al. [102]} & \multirow{2}{*}{2023} & \multirow{2}{*}{GCN-CSS} & Pd = 100\% (\(-8\) dB, Pf = 0.1) & Dynamics of the wireless environment in CR networks. \\ \hline \multirow{4}{*}{[PERSON] et al. [104]} & \multirow{4}{*}{2021} & \multirow{4}{*}{DDQN} & Average cumulative collision rate = 0.06. & Dynamic cooperative spectrum-sensing environments where multiple PUs or multiple SUs can \\  & & & reward = 0.91 & multiple PUs or multiple SUs can \\  & & & & encounter conflicts. \\ \hline \multirow{2}{*}{[PERSON] et al. [106]} & \multirow{2}{*}{2020} & \multirow{2}{*}{DQN} & Modified decision accuracy = 100\% (index of system scenarios = 2) & Dynamic spectrum sensing in wireless networks containing N correlated channels. \\ \hline \multirow{2}{*}{[PERSON] et al. [107]} & \multirow{2}{*}{2021} & \multirow{2}{*}{CQL} & Detection accuracy = 70\% (\(-14\) dB) & Improved detection accuracy of SUs against PUs and reduced energy consumption. \\ \hline \multirow{4}{*}{[PERSON] et al. [108]} & \multirow{4}{*}{2020} & \multirow{4}{*}{DQN+ coordination graph} & Average reward of the SUs in the CG = 0.29 (number of time slots = 1500) & Certain obstacles in the physical environment allow CR to occur under the associated decay and shadows. \\ \hline \multirow{2}{*}{[PERSON] et al. [109]} & \multirow{2}{*}{2022} & \multirow{2}{*}{DQL} & Pd = 90\% (\(-10\) dB) & FC in CSS is subject to data forgery attacks. \\ \hline \multirow{4}{*}{[PERSON] et al. [110]} & \multirow{4}{*}{2019} & \multirow{4}{*}{DQN+HCB-H} & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Each SU gathers information from the environment and other SUs to determine its sensing strategy, which can be structured in two different time slots. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._complete information of the detected signal, but also the SNR in the environment must not be too low. So to summarize, since conventional spectrum-sensing methods usually can only exert their advantages in their specific environments, this also limits their application scenarios. It is also difficult to generalize a single traditional method to other fields. Deep-learning-based spectrum-sensing methods usually automatically extract or learn features in signals and correlation information between signals.