# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 4 Network Model CNN-ST
### CNN Structure  
To extract in detail the radar signal features, the CNN model is designed. Additionally, to maintain the representational ability, the designed CNN removes the nonlinearity in the narrow layers. The structure of CNN in CNN-ST is illustrated in detail in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Operators** & **Layers** & **Size** & **Expansion Ratio** & **Stride** & **Output** \\ \hline Convolution & 1 & 3 \(\times\) 3 & - & 2 & 224 \(\times\) 224 \(\times\) 32 \\ Average pooling & 1 & 2 \(\times\) 2 & - & 2 & 112 \(\times\) 112 \(\times\) 32 \\ Bottleneck & 1 & - & 1 & 1 & 112 \(\times\) 112 \(\times\) 16 \\ Bottleneck & 1 & - & 6 & 2 & 56 \(\times\) 56 \(\times\) 24 \\ Bottleneck & 1 & - & 1 & 2 & 28 \(\times\) 28 \(\times\) 32 \\ Swin transformer & 2 & - & - & - & 14 \(\times\) 14 \(\times\) 64 \\ Swin transformer & 2 & - & - & - & 14 \(\times\) 14 \(\times\) 96 \\ Convolution & 1 & 1 \(\times\) 1 & - & 1 & 7 \(\times\) 7 \(\times\) 160 \\ H-swish & 1 & - & - & - & 7 \(\times\) 7 \(\times\) 160 \\ Convolution & 1 & 1 \(\times\) 1 & - & 1 & 7 \(\times\) 7 \(\times\) 1280 \\ Average pooling & 1 & 7 \(\times\) 7 & - & 1 & 1 \(\times\) 1 \(\times\) 1280 \\ Fully connected & 1 & - & - & - & 1 \(\times\) 1 \(\times\) 8 \\ \hline \end{tabular}
\end{table}
Table 1: CNN-ST parameters.  
Figure 2: Model framework of CNN-ST.  
Figure 3 demonstrates that the designed CNN includes the following: the initial fully convolutional layer; an average pooling layer; two bottleneck convolutional blocks, where the expansion ratio is 1, and the stride length is 1; and a bottleneck convolution block, where the expansion ratio is 6, and the stride length is 2. Within this, the bottleneck convolution block with a stride length of 1 contains the following: a convolutional layer, a batch normalization, an activation function H-swish, an average pooling layer in which a kernel size is 2 \(\times\) 2 and a stride length is 2, a convolutional layer in which the kernel size is 2.  
Figure 3: Structure of CNN model.  
\(1\times 1\) and the stride length is 1, an activation function H-swish, a depthwise convolutional layer in which t the kernel size is 3 \(\times\) 3 and the stride length is 2, an activation function ReLU6, a convolutional layer in which the kernel size is 1 \(\times\) 1 and the stride length is 2, a batch normalization, and a dropout. Finally, a residual connection is utilized. The expansion ratio of the bottleneck convolution block is set as 1. The bottleneck convolution block with a stride length of 2 is the same as the one with a stride length of 1, except that there is no residual connection.  
Depthwise separable convolutions are an essential component of many deep CNN architectures, and are adopted in this paper. A nonlinearity swish was introduced in [44; 45], which was utilized as a replacement for ReLU and improved the classification performance of the dual-component radar signals under different SNRs. The nonlinearity is written as  
\[\text{swish}\ x=x\cdot\sigma(x) \tag{3}\]  
Nonlinearity brings in the non-zero cost and improves recognition accuracy. This is because it is much more expensive to calculate the sigmoid functions on mobile devices. The sigmoid function \(\frac{ReLU6(x+3)}{6}\) is the same as in [46]. The small difference is that ReLU6 is used. Recently, a similar H-swish was proposed in [47], and the hard version of H-swish is defined as  
\[H-\text{swish}[x]=x\frac{ReLU6(x+3)}{6} \tag{4}\]