## 2 Literature Review
### Deep Learning Techniques for Automatic Modulation Classification  
Model-driven approaches mostly choose their features based on experience [33]. FB techniques lose certain original details whilst extracting some statistical features. This affects the performance of categorization, especially in low-SNR circumstances, while the DL-based network may extract highly representative features from the source signals and incorporate feature extraction as part of the classifier training process. Consequently, it surpasses conventional FB approaches in terms of classification performance [34].  
For AMC problems, the first DL technique was used in [7], which consisted of a convolutional neural network (CNN) based on synthetic datasets for model learning, testing, and analysis (known as RML2016.10A and RML2016.04C). Due to the simplistic architecture of the convolution design, the accuracy rate was 71.30% and 87.4% with RML2016.10A and RML2016.04C, respectively. The datasets RML2016.10A and RML2016.04C will be referred to as \(D1\) and \(D2\), respectively. The authors of [35] utilized the dataset of [7] to demonstrate the response of a convolutional neural network to temporal radio signals with complex values. The researchers evaluated the efficacy of radio modulation categorization by comparing naively learned features with expert feature-based approaches that are commonly used today. The study results revealed that the former approach had superior performance. The researchers evaluated the efficacy of radio modulation categorization by comparing naively learned features with expert feature-based approaches that are commonly used today. The study results revealed that the former approach had superior performance.  
The work in [36] used the \(D1\) dataset from [7], and an 80% classification accuracy was achieved through the implementation of a signal distortion correction module (CM). Recently, in DL-based methods, researchers have utilized residual learning techniques that were used for the feature-based approach initially introduced by [PERSON] et al. [37]. The residual structure was employed to overcome the degradation issue and extract discriminative features to achieve sufficient performance. For AMC, the residual learning-based method was deployed in [38]. The ResNet structure was employed to identify the modulation formats for AMC, in which they yield moderate classification accuracy without any network structure adjustments. The authors of [39] proposed an innovative shared model based on a deep learning network using CNN-LSTM utilizing two expert features: wideband frequency modulation (WBFM) and quadrature amplitude modulation (QAM), to improve classification accuracy, and achieved better accuracy with \(D1\). In [40], a DL-based technique for categorizing signal modulation was proposed. The researchers compared multiple DL algorithms by leveraging insights from previous studies and utilizing a diverse set of layers to enhance the existing designs. They employed various techniques such as convolutional layers, dropout layers, and Gaussian noise layers to reduce overfitting and modify the scenario. Additionally, they improved accuracy while minimizing compute time by using a reduced number of filters in each layer. In [41], three efficient models for AMC--a convolutional long short-term deep neural network (CLDNN), a long short-term memory neural network (LSTM), and a deep residual network (ResNet)--were investigated, with the goal of ensuring high accuracy whilst shortening the time needed in order to train the systems.  
In this research, we consider the datasets \(D1\) and \(D2\) from [7] to train and evaluate a convolutional neural network. We propose a less complex AMC method consisting of fewer parameters, compared to the contemporary methods, in an effort to leverage recent progress in neural networks. This advancement will be useful for the implementation of deep learning-based wireless network solutions.