# 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
#### 3.1.4 Application of Residual Neural Networks to Spectrum Sensing  
[PERSON] et al. conducted experiments [65] on several methods based on energy, differential entropy [66], geometric power [67], and the P-paradigm [68] using neural network structures such as DNNs, CNNs, ResNet, MLPs, etc., in order to compare the performances and study the optimal combinations of neural networks with signal processing methods. The experiments in this paper demonstrate that as the depth of a CNN increases, its performance also increases, but the vanishing gradient problem also occurs, so residual blocks are introduced to solve this problem. The experimental structure shows that the performance of all DNNs is not very different at moderate SNRs, but the performance of ResNet is slightly better, and the performance of ResNet based on ES features is better than that of ResNet based on alternative features. In order to solve the problem of predicting the load of spectrum sensing on the joint spatio-temporal domain, the authors [PERSON] et al. [69] proposed a different network model, based on CNN and ResNet, that can accurately predict the spectrum usage in large regions. In order to address the incomplete power matrix, two approaches, matrix completion [70] and local interpolation [71], were used to reconstruct it, and the performance differences between them were analyzed. In order to more adequately test the proposed model, experiments were performed in two different scenarios: a single transmitter scenario and a multiple transmitter scenario. The experimental results showed that the proposed model provided solid predictions of the overall spectrum usage patterns, while its parameter settings depended on the characteristics of the dataset. In 2022, the authors [PERSON] et al. considered the use of STFT for feature extraction of signals for spectrum sensing and proposed a spectrum-sensing method based on short-time Fourier transform with improved residual network (STFT-ImpResNet) in [72]. The main idea of the proposed method is to process the signal through STFT to obtain a 2D time-frequency matrix and normalize it to obtain a grayscale image as input to the network. The network used is the authors' improved ResNet, with the remaining bottleneck block as the residual block, which reduces the amount of parameter computation and improves the residual block to enhance the feature extraction ability, and finally, replaces the fully connected layer with global average pooling (GAP) to again reduce the training parameters and the risk of overfitting. The experimental results show that STFT-ImpResNet outperforms SVM, ED, and STFT-CNN in terms of detection probability at \(-19\) dB SNR for a certain false alarm probability, with a 94.5% detection probability for this model for a false alarm probability of 0.01. Figure 12: Schematic diagram of the residual module. STFT cannot vary with time and frequency as required, resulting in a window fixation problem. Therefore, unlike the above authors, who combined STFT with ResNet, in [73], [PERSON] et al. proposed a spectrum-sensing method (WT-ResNet) based on combining wavelet transform and ResNet for signals with non-smooth features, which is also a data-driven algorithm based on fully blind detection without a priori information. Since numerous signals are non-smooth in practical situations, the drawback of using STFT to implement non-smooth signals is the restriction that the window function cannot be frequency-shifted. Therefore, the authors consider the use of the wavelet transform (WT) [74] method. By making WT available to provide wavelet bases, the wavelet bases can be varied with the desired frequency and time, thus overcoming the problem of the fixed window function. The main idea is to process the signal through WT to obtain the time-frequency matrix and, thus, as a statistic, use the BP algorithm to train the model in the offline phase and use the received data in the online phase for detection.