## 3 Proposed Design
### Squeeze-Excitation Network
The suggested design is based on four residual learning blocks and SE blocks. Firstly, the input reshape layer is linked with a residual block, which is used for making the dimension of data compatible with the model input without changing the data. The dimensions of the input data are \(2\times 128\), consisting of in-phase and quadrature (I and Q) samples. The modulation signal is sampled with a \(l=128\) sample rectangular window that separates the data for training and testing. The architecture based on residual learning consists of three convolution layers. These convolution layers of residual learning are interconnected and are able to unfold common features and reduce the spatial dimension. Later on, two layers of asymmetric convolution kernels are stacked, containing kernel sizes of \((3,3)\) and \((1,1)\), to reduce the number of trainable parameters without compromising the quality of the retrieved features. This is further followed by the batch normalization and max pooling layers, with a pool size and stride of \((2,2)\). The residual blocks are applied to retrieve high-level features and perform identity mapping. Each block of convolution layers performs feature extraction. Followed by the residual block, the SE blocks are deployed, consisting of global average pooling with two fully connected layers, resulting in a decreased number of parameters. The SE blocks contain the dimension sizes of the output channel, which are 128, 64, 32 and 16, and the transformation ratio \(r\) is set to 16. The transformation ratio is a key hyperparameter that enables adjustment of the computational burden and performance of the SE blocks in the model. Therefore, to provide even better efficiency, the network then passes through the fully connected layer (selu) and the activation function (softmax). In order to increase the accuracy of the model, a skip connection is employed to connect the SE blocks in order to prevent the model from degradation issue and enhance the learning and representational properties of the deep networks. Various features gathered from each block and the informational identity retained throughout the network via the skip connection approach are merged to enhance the classification model. As a result, during the network training phase, the proposed approach can overcome the problems of vanishing gradients and overfitting. At the end, the last layer contains the two fully connected layers and the softmax layer. The number of neurons in the FC layer is adjusted to the number of modulation classes of a stated dataset. The number of parameters is directly related to the computational complexity; the proposed model, with only 253,274 parameters, is extremely efficient in terms of computation burden. Reducing the number of residual and SE blocks, as well as the kernel size in convolution layers, would result in even lower number of parameters; however, because the system is not dense enough to generate highly discriminative features at multiscale interpretations of feature maps, the classification performance will be reduced. The latent features of the proposed model have been extracted from the flattened layer and visualized using the t-distributed stochastic neighbor embedding (t-SNE). The t-SNE of the input space and the latent feature space are calculated and visualized as presented in Figure 5a,b, respectively. The t-SNE plots of the input and test feature space illustrate the  
Figure 4: Overall block. superior discriminating feature extraction capability of the proposed network. However, there are a few instances of misclassification, such as the overlap in the QAM16 and QAM64 embeddings. It is established in [37] that as the network depth increases, the accuracy saturates and further degrades; therefore, increasing the number of layers in a network may lead to more training errors. Residual learning addresses this by identity mapping where a rather shallow network can achieve an equivalent, if not superior, performance compared to its deeper counterparts.