## 1 Introduction  
In wireless communication, the complexity of the environment and the signals is rapidly increasing. A vital phenomenon in ad-hoc networks such as cognitive radio (CR) and software-defined radio (SDR) is automatic modulation classification (AMC) [1]. In modulation, information is typically communicated between the transmitter and receiver in a standard communication environment [2], whereas devices in CR transmitters autonomously choose modulation schemes based on external contexts, and CR receivers should independently verify signal modulation patterns [3]. AMC assists the CR receivers in identifying the type of modulation selected by the transmitter. In SDR, AMC is applied to quickly respond to diverse and evolving communication networks whilst avoiding protocols overhead. The current technology in a cognitive jamming scenario involves the automatic discovery of the modulation schemes utilized by both favorable and adversarial signals [1]. While military technology has always been a driving force behind the advancement of AMC, commercial applications such as interference detection and spectrum sensing are also widespread [4]. The development of the 5 th generation of telecommunication networks (5G), which is predicted to result in the proliferation of end devices in use and congestion of the electromagnetic spectrum, has sparked renewed interest in AMC. Without knowing the system parameters, AMC is used to determine the transmitter's modulation configuration from the received signal as shown in Figure 1.  
Signal, noise, and channel models have a significant effect on the classification result. Therefore, they are all used to develop AMC techniques. When the expected signal model or noise model does not fit the actual signal or noise, the corresponding classification model fails to perform adequately. A more sophisticated model may be expected to mostly reduce the gap with the real scenario. There are many unspecified parameters to evaluate, which leads to greater estimation mistakes, and the additional computing complexity cannot be overlooked. Furthermore, certain situations, such as molecular communications may not have manageable predictive methods, severely decreasing the classification accuracy of the typical design classifier [5]. Due to computing complexity, they have been restricted in their relevancy to a wider range of fields. Data-driven AMC methods have been designed to address these complexities [6]. To investigate the aforementioned complexities, we present a data-driven AMC technique using deep learning (DL) in order to classify the modulation of the signal. To acquire this, we compared the performance of various DL algorithms from preceding works with the proposed technique. The contributions of this paper are mentioned as follows:  
* We employed the property of a residual learning block possessing significant representational capability in order to acquire latent information from received signals repeatedly, enhancing classification accuracy. * Additionally, we utilized the squeeze-and-excitation network block that is customized for the AMC task. This takes full advantage of modeling channel interconnections and iteratively adjusts channel-wise characteristic responses to boost efficiency. * To improve the architecture, we stacked various type of layers, such as the conv2D layer, the batch normalization layer, and the global average pooling layer, which were used to extract the features. * Two datasets, namely RadioML 2016.04C and RadioML 2016.10A, were utilized from [7] to compare the effectiveness and generalization ability of AMC with diverse architecture configuration. * The datasets included eleven types of modulated signals, namely QPSK, AM-DSB, AM-SSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, and WBFM. They were all utilized to train the network.