# A Review of Research on Spectrum Sensing Based on Deep Learning
## 3 Deep-Learning-Based Spectrum-Sensing Methods
### Application of Convolutional Neural Networks to Spectrum Sensing
#### 3.1.2 Spectrum-Sensing Method Based on Convolutional Neural Network
The experimental results demonstrate superior performance in rigorous channels compared to methods such as support vector machines (SVMs) and cyclostationary feature detection (CFD). The model employs supervised learning, and future improvements in its performance can be explored using unsupervised learning techniques. The authors [PERSON] et al., in [61], considered spectrum sensing in the application scenario of a multi-antenna transmitter. The researchers propose a deep spectrum-sensing (DS2 MA) system that employed a multi-antenna receiver with multiple antennas. The innovative idea of the paper is to construct a correlation matrix through the autocorrelation function of each antenna and the antenna-to-antenna inter-correlation function, which is used as an input to the proposed CNN model, and the cross-entropy function [62] is used as the loss function in the training phase. The simulation results show that at low SNRs, the model proposed in this paper using the autocorrelation function with the mutual correlation function optimizes the performance of the DS2 MA model with a detection probability of 0.4 over the DS2 MA model using only the autocorrelation function, and also improves dramatically compared to the CM-CNN and the conventional ED. Cognitive radio and efficient dynamic spectrum access play an essential role in wireless network communications for post-disaster reconstruction. In 2023, in order to improve the previous cellular network system by providing hotspots and network coverage during post-disaster scenarios, [PERSON] and [PERSON], in a paper presented at the EAI International Conference on Innovations in Sustainable Cognitive Computing Big Data [63], proposed a CNN-based cooperative spectrum-sensing approach for flying cell towers (FCTs). Flying cell towers are multiple UAVs deployed as spatial base stations and connected to terrestrial cell towers, and the data provided by these devices is used to train CNNs to better predict terrestrial user behavior. They exploit the cyclic autocorrelation function, spectral correlation function, and energy function to extract diverse features of the cyclostationary signal, enabling the discrimination of the PU signal from noise. Their simulation results show that with a training rate of 300, the model achieves 90.68 percent accuracy and achieves performance improvement in the low-SNR range. In this paper, we summarize the CNN-based spectrum-sensing methods in the past five years; see Table 4. CNNs have achieved great results in obtaining time-frequency information through signal spectrograms for spectrum sensing. Moreover, better detection performance can be achieved by obtaining the statistical feature quantity of the signal for learning to realize spectrum sensing through methods such as SCF or covariance matrix. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Input** & **Model** & \begin{tabular}{c} **Detection** \\ **Probability** \\ \end{tabular} & **Advantage** \\ \hline \multirow{3}{*}{[PERSON] et al. [53]} & \multirow{3}{*}{2021} & Time–frequency & \multirow{3}{*}{STFT-CNN} & 90\% (\(-15\) dB, \(\text{Pf}=0.1\)) & \begin{tabular}{c} Considerable SNR \\ robustness. \\ Independent of \\ signal and \\ noise assumptions. \\ \end{tabular} \\ \hline \multirow{3}{*}{[PERSON] et al. [54]} & \multirow{3}{*}{2022} & Spectrogram & \multirow{3}{*}{CNN} & Can efficiently discriminate \\  & & images of the & & between signal and \\  & & received signals & & noise at \\  & & & & different SNRs. \\ \hline \multirow{3}{*}{[PERSON] et al. [55]} & \multirow{3}{*}{2022} & Spectrogram of the & \multirow{3}{*}{S-CNN} & 90\% (\(-8\) dB, \(\text{Pf}=0.1\)) & The method is \\  & & signal obtained by & & \
\begin{tabular}{c} \(\text{Pf}=0.1\)) \\ \end{tabular} &
\begin{tabular}{c} The method is \\ insensitive to the \\ modulation order. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A review of spectrum-sensing algorithms based on convolutional neural networks.