# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
## 3 Methodology  
### SCN Architecture  
Figure 3 presents the architecture of the SCN, which includes three main parts, the residual backbone, the FPN neck, and the RegNet head. The inputs of the SCN are the 1D broadband power spectrum, and the model outputs are PSD and BW regressions. Moreover, all the local maximums of PSD regression are the subcarrier FC positions. * **The Residual backbone** The residual backbone in SCN is modified by the deep residual network (ResNet) [28], which is widely used in the computer vision field. Additionally, it has emerged as a family  
Figure 3: The architecture of SCN-13\(\times\) includes three main parts, the residual backbone, the FPN neck, and the RegNet head. Figure 2: The detection process of the carrier signal detection in broadband power spectrum. The whole process mainly includes two steps. First, the Welch power spectrum estimation method is utilized to estimate the broadband power spectrum. Then, the proposed SCN regresses the FC and the corresponding BW of all the subcarriers of the broadband power spectrum. of extremely deep architectures showing compelling accuracy and excellent convergence behaviors. In our work, since the inputs are a batch of 1D broadband power spectrum sequences, all of the parameters are suitable for setting the dimension of the inputs. Meanwhile, we still utilize the 2D Conv application interface (API) and fix the features to the width channel. For instance, if the Conv kernel size is \((1,k)\) with a stride of \((1,s)\), then the padding size would be \((1,(k-1)/(2\times s))\). As can be seen in Figure 3, we add a convolutional block attention module (CBAM) [29] for better feature extraction prior to the residual blocks. Contrary to the original CBAM, we directly concatenate the average global pooling (GAP) and maximum global pooling (GMP) features in the channel attention module (CAM). Additionally, we use a \(1\times 1\) Conv to replace the shared multilayer perceptron (MLP). Moreover, we set the groups to four to reduce the computing complexity. In Figure 3, the residual layer in SCN is contrary to the original residual layer. Herein, we add a simplified CAM (S-CAM) prior to the last non-linear activation of the residual layer. Compared with the CAM, only GAP is used prior to the \(1\times 1\) Conv in S-CAM. Additionally, we use only one residual block and downsample the feature length in each residual layer, thus the downscale increases by two times. In our work, the input spectrum length is set to 32,768, which is considerably longer than the 2D image size in the object detection tasks. Therefore, we can set the downsample times (M) between 6 to 13 and name the whole corresponding model as SCN-M\(\times\). In the architecture of SCN-\(13\times\) and the residual backbone, the number in the boxes represents the strides to the input spectrum length, which increases with the number of residual layers and is equal to \(2^{M}\), as shown in Figure 3. Moreover, the output features of each residual layer are called PM. Furthermore, to reduce the complexity of the original residual layers, the number of feature maps is set to \(64+32\)\(M\). * **The FPN Neck** Feature pyramid structures (FPN) are essential in recognition systems to detect objects at different scales [23]. This top-down architecture builds high-level semantic feature maps with lateral connections at all scales. We utilize FPN as the features fusion neck module, as shown in Figure 3. With the skip-connections, the inputs of FPN are the outputs of the residual layers. We use the bilinear mode in the upsample layers and perform the last regression at four strides to the input spectrum length. All the Conv layers in the FPN head contain 256 channels. This architecture efficiently fuses all the top-to-bottom scale features.