# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 1 Introduction  
Adapting the modulation used for changing channel conditions is a crucial way to maximize the throughput of a communication link and maintain continuous link reliability in a mobile environment [1; 2; 3; 4]. By changing the modulation method, the transmitted signal can adjust to the changing channel environment, minimizing the difference between channel capacity and throughput without sacrificing robustness [5]. Automatic modulation classification (AMC) [6; 7; 8; 9; 10; 11] is a crucial technology for non-cooperative communications, serving as an intermediate step in signal detection and demodulation in communications. AMC has a wide range of applications in both military and civilian domains. In the military field, AMC can interfere with the enemy modulation signal when identified, while in the civilian field, it is the basis for spectrum allocation and signal demodulation [12]. Given the current increasingly complex electromagnetic environment, AMC technology has garnered significant attention from researchers. The general AMC process comprises two primary steps: signal preprocessing and classification. Signal preprocessing involves noise reduction and estimation of signal parameters, such as carrier frequency and signal power. The classification step in AMC methods can be broadly categorized into likelihood ratio-based methods [13; 14] and feature-based methods [15; 16; 17]. The likelihood ratio-based method [18] uses the modulated signal to establish the likelihood function, and then compares the calculated likelihood ratio function value with the threshold value to complete the signal classification. The likelihood ratio-based AMC method yields the optimal solution at the cost of high computational effort. It has high computational complexity and requires channel state information ofthe communication system. The feature-based AMC method has relatively low computational complexity but relies heavily on specialized knowledge to construct complex feature engineering. This includes higher-order statistics-based, parametric statistics [19], constellation diagrams, cyclic spectrum [20], time-frequency transformation domain, and others. Moreover, the classifier is designed for specific features, such as artificial neural networks (ANNs), support vector machines (SVMs), K-nearest neighbors (KNNs), etc. In recent years, deep learning techniques have made significant breakthroughs in the fields of image [21] and natural language processing [22]. Hence, many researchers have attempted to apply deep learning techniques to AMC. Artificial intelligence has numerous applications in wireless communication, including signal detection [23; 24; 25], channel estimation [26; 27; 28; 29; 30], radio frequency fingerprint identification [31; 32; 33], and beamforming [34; 35]. Prior to the development of artificial intelligence, classical methods mainly designed features through signal processing technologies, and then machine learning-based classifiers were applied for identification. However, there are many shortcomings of the characteristics designed by artificial knowledge and experience, such as weak performance, poor environmental adaptability, and difficulty in dealing with increasingly complex and numerous wireless transmitters. From a processing flow perspective, AMC using deep learning methods is primarily based on image transformation and original sampled signals. Image transformation-based AMC involves converting the original sampled signal into an image and then using deep learning to classify modulations, such as images. The commonly used image transformations include time-frequency diagrams [36; 37; 38], constellation maps [39], and others. On the other hand, deep learning is used to perform AMC on the sampled signal [40]. The sampled signal already contains the necessary information for AMC, without increasing the computational effort of conversion to an image, making it suitable for application in spatially embedded computers.