# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
###### Abstract
Another approach, based on a CNN, was presented that considered more realistic signals with four convolutions and two dense layers [27]. The key findings of the CNN layer's width and the effect of the depth in improving the accuracy of the model were proposed by [PERSON] and [PERSON] [28], who presented a CNN-LSTM model. Considering the long symbol rate, another approach, based on a CNN, was presented, which used pretraining and fine-tuning of the presented model [29]. Feature extraction from a skip connection was presented using an asymmetric kernel for a multiscale convolutional block to improve the performance [30]. A 1D and a 2D CNN, along with the LSTM and deep-neural-network (DNN) layer architectures, were presented, which showed feature extraction in both the time and space domains [31]. A parallel fusion framework using a 1D CNN to extract amplitutional and phasic features was proposed [32], achieving improved accuracy and an efficient convergence speed. Another CNN-based approach used the 5G communications system, which exhibited low computational complexity by applying different types of layers for regularization, dropouts, and Gaussian noise layers, thus minimizing the overfitting issue [33]. However, the accuracy of the model was very low [1]. Another CNN-based approach involved combining seven convolutional layers, an LSTM layer, and a DNN to produce a CLDNN [34]. This was compared with a dense connection between the layers (DenseNet) and a residual neural network (ResNet). It was concluded that the CLDNN method was the best fit for the AMR problem. A classifier was proposed using a two-layer gated recurrent unit neural network (GRU) [35]. A three-stage architecture that combined the shallow CNN, GRU, and DNN was proposed, which incorporated pooling layers, a dropout, and several other layers to overcome overfitting [36]. A lightweight phasic parameter estimator model was proposed that extracted features from the spatial and temporal domains using a CNN and a GRU [37]. It is also evident from the literature that the performance of the AMR method relies on the type and combination of input signals after passing through various feature extractors. These may include spectral features [38], statistical features [39], constellation images [40; 41], SPWVD images, BJD images [14], I/Q signals [24; 27; 28; 30; 34; 36; 37; 42], amplitudes/phases [25; 26], I/Q signals with amplitudinal and phasic features [43; 44], and I/Q signals with individual I and Q signals [31]. As the received signal is available in the form of an I/Q signal, it is easy to obtain its individual signals rather than convert them to different spectral features or constellation diagrams, which involve a computationally complex mechanism for real-time applications [31]. Therefore, in this study, an I/Q signal was used alongside individual I and Q signals by separation. Recently, many researchers have presented systematic literature reviews on modulation classification [1; 45; 46; 47; 48; 49; 50; 51; 52]. Notable work has been presented on several AMR algorithms, datasets, and results in [45]. The main contributions of this study are as follows:  
1. A hybrid architecture for AMR is proposed that combines a convolutional transformer-based DL classifier to minimize computational complexity while retaining classification performance;
2. The proposed algorithm is investigated by conducting experiments with different batch sizes, optimizers, and model configurations. The remainder of this paper is organized as follows. In Section 2, the proposed hybrid convolutional transformer classifier (HCTC) model is introduced along with the signal information. The implementation of the proposed model is briefly explained in Section 3. Section 4 presents the experimental results, and Section 5 presents additional experiments on the proposed model, with different configurations and batch sizes. Section 6 explains open issues and future work, followed by the conclusion in Section 7.