# A Review of Research on Spectrum Sensing Based on Deep Learning
## 2 Spectrum-Sensing Methods
### Single-Node Spectrum Sensing
#### 2.2.3 Matched Filter Detection  
Matched filter detection is a more desirable detection method in the context of additive white Gaussian noise. This detection technique can better accommodate the variability in the amplitude, frequency, and phase of the signal, and has high detection precision and accuracy. Moreover, it can improve the robustness and reliability of detection by  
Figure 4: Flowchart of digital implementation of cyclostationary feature detection. combining multiple matched filters to meet different practical needs. It can be expressed by the following binary judgment, Equation (5):  
\[ \begin{cases}H_{0}:T(x)\equiv\sum_{n=1}^{N}x(n)s^{*}(n)<\gamma\\ H_{1}:T(x)\equiv\sum_{n=1}^{N}x(n)s^{*}(n)\geq\gamma\end{cases}  \tag{5}\]  
The matched filter detector first calculates the correlation between the hypothesized signal and the unknown signal and then compares the output with a preset threshold. That is, the output is used in Equation (5) for binary judgment to determine whether the main signal is in state \(H_{0}\) or state \(H_{1}\). The core idea is to perform correlation operations between known and unknown signals via a predefined filter to detect the presence of a specified frequency and modulation signal in a signal. The digital implementation flowchart for matched filter detection is shown in Figure 5 below. This approach has a wide range of applications in mobile communications and radar signal processing. Although the detection performance of matched filters is relatively good, it requires prior information from the PU to design a specific filter to handle different radio environments, and hence its applicability is relatively low. [PERSON] et al., in [34], proposed a hybrid matched filter detection (hybrid MFD) method based on the combination of the energy detection method and matched filter detection method, and simulation experiments on MATLAB. The results show that the proposed hybrid matched filter detection method outperforms the traditional matched filter detection method in detecting the presence of PU. While [PERSON] investigated the correlation between neighboring received signals under dispersive channels with the idea that different channels have different impacts on sensing, and proposed a blind matched filter (BMF)-based spectrum-sensing algorithm, in [35], which does not need PU a priori information compared to the traditional method and based on the correlation between signals with different time delays, and proposed an improved BMF (IBMF) algorithm. The results of the study showed better detection performance compared to the traditional matched filter detection method. To improve the detection capability of matched filters, [PERSON] in [36] proposed a multiple antenna element (MAE)-based and matched filter (MF)-based spectrum-sensing technique, which improved the signal to noise ratio of the received signal through the enhancement in the sensing capability of multiple antennas and used the likelihood ratio for detecting the PU signals. The simulation results showed that the proposed method in this paper outperformed the single-antenna-element-based matched filtering (SAE/MF), based on a single antenna element, and other existing techniques, with excellent performance. The advantages and disadvantages of the three traditional spectrum-sensing methods described in this section are explained and summarized in Table 2 below. Figure 5: Schematic of matched filter detection. Applications of single-node spectrum sensing in real-world communication environments will also face some situations that require further research in the future, such as the following:  
1. PU signal uncertainty: Under the current spectrum management mechanism, each PU has absolute priority to use its working frequency band, and if a local node or secondary user requires prior knowledge of a PU's signal, such as modulation coding method or timing, the PU is not obliged to provide it. Therefore, it is very difficult for the SUs to perceive the signal uncertainty in the PUs in the real spectrum. 2.