## 2 Related Works  
The main focus of this study is determining the type of received signal and its modulation in a Satcom scenario based on a supervised ML-based classification using MLP. ML methods have been used in different research studies for RFI classification and AMC regarding their flexibility in data processing and accuracy in the designed models. As in [12] an Artificial Neural Network (ANN) is proposed for a jammer detection in wideband radios. To this end, spectral correlation is used as the feature extraction technique. According to the obtained results, the proposed technique performed efficiently on SNR values down to \(-3\) dB. In [16], three robust ML-based classifiers such as MLP, Support Vector Machine (SVM) and Random Forest (RF) have been deployed to detect jamming attacks in the 5G wireless network. According to the results RF has slightly more precise classification performance. Reference [17] presents an efficient MLP-based approach to recognize one of the jamming attacks known as Denial of Service (Dos). Moreover, an unsupervised correlation-based feature selection method using the PearsonCorrelation Coefficient (PCC) is used to select more informative features. The authors in [18], present a robust MLP technique combined with a GA-based feature selection to detect various intrusions such as Remote to Local (R2L), User to Root (U2R) and DoS attacks. In [19], an efficient MLP design has been developed to recognize different intrusions. In [20], an efficient MLP is proposed to determine either a DoS attack exists in the wireless network or not. In our previous work [21], we developed an MLP in different learning modes, namely Stochastic Gradient Descent (SGD), full-batch, and mini-batch. As the results show, the proposed technique can precisely classify four received signals, including SoI and a combination of SoI with three other jamming signals. Moreover, we deployed PCA to select more appropriate features to optimize the classification process. In this work, we intend to propose a solution to determine not only the type of received signal but also Automatic modulation classification (AMC). AMC is a significant procedure for present and next-generation communication networks and facilitates the demodulation process at the receiver side [22]. Therefore, modulation recognition is an intermediate step between signal detection and demodulation. AMC is a system that automatically identifies the modulation type of the received signal [23], and the technology is widely used in various applications, such as dynamic spectrum management [24] and interference identification in CR [23]. In [25], a robust hierarchical classification based on MLP has been presented to recognize the modulation types of communication signals. As was thoroughly discussed in [14], AMC can be implemented regarding either a likelihood-based or a feature-based scenario. The disadvantages of a likelihood-based approach may include high computational complexity and sensitivity to impairments, such as phase and frequency offsets. The feature-based AMC, maybe comparatively more efficient [23] as it leverages robust extracted features such as an instantaneous amplitude, phase, and frequency [26], cyclostationary features [27], higher-order cumulants [28], and spectral correlation features [22]. In [23; 29], the authors investigated different modulation types of recognition in a digital video broadcasting scenario based on higher-order cumulants and MLP. AMC-based higher-order cumulant features are the primary concern of this study, inspired by [23] with proposing an approach based on higher-order cumulants of wavelet coefficients and MLP. Moreover, this study also applies PCA to select features that are more informative to accelerate the classification process. The results indicate that this method precisely classifies various modulation types with an accuracy above 99% at different SNRs ranges (\(-4\sim 4\)) dB [23]. In [30], an AMC-based DT approach and higher-order cumulants are proposed.