###### Abstract  
This study proposed a two-stage method, which combines a convolutional neural network (CNN) with the continuous wavelet transform (CWT) for multiclass modulation classification. The modulation signals' time-frequency information was first extracted using CWT as a data source. The convolutional neural network was fed input from 2D pictures. The second step included feeding the proposed algorithm the 2D time-frequency information it had obtained in order to classify the different kinds of modulations. Six different types of modulations, including amplitude-shift keying (\(ASK\)), phase-shift keying (\(PSK\)), frequency-shift keying (\(FSK\)), quadrature amplitude-shift keying (QASK), quadrature phase-shift keying (QPSK), and quadrature frequency-shift keying (QPSK), are automatically recognized using a new digital modulation classification model between 0 and 25 dB SNRs. Modulation types are used in satellite communication, underwater communication, and military communication. In comparison with earlier research, the recommended convolutional neural network learning model performs better in the presence of varying noise levels. m 202211620221619 September 202210 November 202215 November 202211619 September  
of the received signals allows for the determination of the modulation type. Without a doubt, high-quality features may provide reliable performance at a reasonable price. Recent research [7] has previously looked at some of these properties, including WT [8], cyclostationary features [9], and higher-order cumulants [10]. Support vector machines (SVMs) [11] and D-Trees [12], as well as Random Forests (RFs) [13] and KNNs [14], were frequently used for AMC systems in earlier studies. Unfortunately, these approaches are time-consuming since the extraction of handmade features requires in-depth technical knowledge and domain experience. Deep learning (DL) has recently received a lot of attention due to its success in a number of applications, including speech recognition [15], emotion analysis [16], and computer vision [17]. In contrast to conventional data-analysis and processing techniques, the DL's capacity to automatically represent complex, high-dimensional data without the need for manual features is of critical relevance [18]. Because of this, we have noticed it encroaching on other domains, such as communications [19]. It is significant to note that some engineers have completed AMC assignments using DL with success. So far, several methods have been presented using several different techniques based on deep learning, including LSTM networks [20; 21; 22; 23], deep convolutional networks [21; 24; 25], RNN [26; 27], etc. To clearly define the complicated relationships of time-correlated signals across Rayleigh fading channels, along with different additive noise circumstances, an RNN-based AMC technique is examined in [28]. A sophisticated RNN architecture, known as the long short-term memory (LSTM) network [29], has been made particularly for learning the long-term dependencies in the time domain of signals with varying-length modulation. CNNs are capable of extracting more significant discriminating features from multiscale feature representations for the multiclass classification challenge when compared to RNNs and LSTM. In the newest 24-modulation DeepSig dataset, a compact CNN combining numerous residual convolutional stacks, used to gather more relevant information from multilevel representational feature maps, greatly increases the classification rate. Without feature engineering expertise, DL outperforms ML in recognition performance [30]. For network training and classification tasks, the authors in [31] employed constellation diagrams and the AlexNet CNN model. Additionally, the Caffe framework was used throughout the whole modulation classification process. Based on ACGAN, data augmentation was used in [32]. Nevertheless, [PERSON] et al. Images with grid-like topologies were created using two pretrained models for AMC (AlexNet and GoogLeNet) by [33].