# A Review of Research on Spectrum Sensing Based on Deep Learning
## 2 Spectrum-Sensing Methods
### Single-Node Spectrum Sensing
#### 2.2.2 Cyclostationary Feature Detection  
The cyclostationary feature detection technique is a common method in spectrum sensing, which uses the statistical characteristics of modulated signals to show the periodicity of detection [31]. Since most random signals have first- or second-order statistical characteristics after sampling, modulation, multiplexing, and other periodic transformations, additive white Gaussian noise (AWGN) is a broadband random smoothing process without  
Figure 3: Flowchart of energy detection.  
cyclic smooth characteristics. Therefore, the cyclostationary feature detection technique can use the cyclostationary feature of the frequency-domain signal to separate the authorized signal from the noise signal and realize spectrum sensing. The digital realization flowchart of cyclostationary feature detection is shown in Figure 4.  
The calculation process of the cyclostationary feature is as follows, Calculate the periodic autocorrelation function of the signal:  
\[R_{X}^{\alpha}(\tau)=\lim_{T\rightarrow\infty}\int_{-\frac{\tau}{2}}^{\frac{ \tau}{2}}x(t+\frac{\tau}{2})x(t-\frac{\tau}{2})e^{-\beta\pi\pi t}dt, \tag{2}\]  
\[S_{X}^{\alpha}(f)=\int_{-\infty}^{+\infty}R_{X}^{\alpha}(\tau)e^{-\beta\pi/ \tau}d\tau, \tag{3}\]  
\[S_{X}^{\alpha}(f)=\lim_{T\rightarrow\infty}\lim_{Z\rightarrow\infty}\frac{1}{TZ }\int_{-\frac{\tau}{2}}^{\frac{\tau}{2}}X_{T}(t,f+\frac{\alpha\tau}{2})X_{T}^{ \alpha}(t,f-\frac{\alpha\tau}{2})d\tau \tag{4}\]  
The obtained spectral autocorrelation function is then subjected to M-times mean computation. Finally, the output is subjected to feature detection to determine whether it is the primary user signal or not and to determine the state of the primary signal.  
Compared with the energy detection method, the cyclostationary feature detection method has more stable performance against the background noise uncertainty and can operate under lower SNR conditions than the energy detection method.  
It should be noted, however, that the performance of the method will be worse when the signal due to background noise also has the cyclostationary feature. On the other hand, because of the need to detect the cyclostationary feature of the signal, Equations (2)-(4) show that the statistical feature requires the Fourier transform of the cyclical autocorrelation function, which has a long computation time and high computational complexity, which limits its applicability.  
Existing research on cyclostationary feature detection methods is focused on combining optimization with higher-order statistical features based on other algorithms. [PERSON], in [32], proposed a new cyclostationary feature detection method based on the Gaussian kernel least mean square algorithm (KMLS), focusing on a segment of a 5G network band, and designed a suboptimal detector based on the KMLS. The simulation results obtained using an experimental 5G communication setup were verified, showing the effectiveness of the proposed method. In Ref. [33], scholars extracted the cyclostationary feature from broadband signals from sub-Nyquist samples in order to identify modulation schemes with low computational complexity, and the proposed sub-Nyquist sampling structure of the wideband modulated converter (MWC) was used to perform spectrum detection with good results.