## 2 Literature Review
### Feature-Based (FB) Method  
FB methods, which are widely used for AMC, extract features from the received signal and feed them into a classification system [12]. They are found to outperform LB techniques in terms of reliability and computational overhead. To detect the modulation type of a signal, FB methods have been employed on a set of data description features, which assist in formulating decisions [6]. There are two steps to the creation of FB modulation classification method: preprocessing and a classification algorithm. 1. Preprocessing: This stage is responsible for extracting features from the received signal. Different features can be chosen based on various circumstances and predictions. Certain immediate aspects of the signal, such as instantaneous signal power, frequency, phase, amplitude, and so on, are retrieved during the feature extraction phase [13]. As a result, these characteristics transform the raw data into patterns that must be learned by the classifier for the purpose of recognition. 2. Classification Algorithm: The classification algorithm utilizes the features from the preprocessor as an input, and outputs the modulation type of the signal for each received signal. The FB approach creates a higher-dimensional environment in which signal characteristics can be isolated using a hyperplane [14]. Among the most commonly utilized features in FB approaches, high-order cumulants [15], wavelet transform [16], and cyclo-stationary features [17] are principally employed for feature extraction. In noncooperative circumstances, these statistical characteristics are often combined to improve reliability. A classifier processes and compares the obtained statistical properties of the incoming signal, with preset limits, to identify the modulation type in the classifier step. Comparison Between Likelihood-Based and Feature-Based Method  
In contemporary research, several classifiers have been presented, notably maximum likelihood [18], distribution test-based [13], and machine learning-based classifiers. Notably, the efficiency and statistical complexity of each classifier are routinely measured. For every realistic implementation, choosing the proper classifier is crucial [19]. In contrast to the processes employed in likelihood-based AMC, statistical methods for feature extraction are often particularly less complex in terms of processing cost. Due to their intrinsic low complexity and the use of blind modulation schemes, feature-based techniques are increasingly popular for real-life scenarios, which demand no extra information about the signal or channel [20]. Therefore, owing to the aforementioned features, these two typesof classifiers have dominated AMC for decades. In comparison, the LB classifier can find the best solution using Bayes sense to reduce the likelihood of incorrect classification. The FB method can achieve high reliability for recognizing basic modulation types such as BPSK and QPSK [21]. Moreover, for assessing unknown values, LB classifications possess a high computing complexity [22], whilst the FB classifier's efficacy is significantly impacted by feature cohesion. Conventional AMC innovation has always relied on likelihood- and feature-based techniques. It seeks to develop more effective features and classifiers [23]. With the advancement of artificial intelligence in the past few decades, deeper learning-based algorithms have been utilized to tackle the AMC issues presented in [24; 25]. With the data presented in [24], a convolutional neural network (CNN) for AMC and investigated structure optimal depth was proposed. Furthermore, in [25], a data-driven model based on LSTM was presented to overcome the AMC problem. A design composed of LSTM and CNN modules was considered as a solution for achieving high efficiency of AMC with different SNR regions. AMC approaches usually depend on feature extraction to reduce the complexity of signal data and classification accuracy [21].