## 2 Related Works  
With the rapid advances of AI technology, DL is also increasingly being applied to the field of RFI and modulation classification. To name a few, in [13], a robust DI-based technique is proposed known as faster region-based convolutional neural networks (Faster R-CNN) for interference and clutter detection in a high-frequency surface wave radar (HFSWR). To this end, the Range-Doppler (RD) spectrum image is used as the input of the designed network. As a result, the proposed method has a high classification accuracy and a decent detection performance [13].  
[PERSON] and et al. have proposed a CNN-based strategy named RFI-Net to detect interference in a five-hundred-meter Aperture Spherical radio Telescope (FAST) [14], that can outperform other techniques such as the U-Net model based on a CNN architecture, k-nearest neighbors (KNN), as well as Sum-Threshold. In [15], two DL-based strategies are used for jamming attack detection, namely deep convolutional neural networks (DCNN) and deep recurrent neural networks (DRNN). In this research, two different jamming attacks, namely, classical wide-band barrage jamming and reference signal jamming, have been analyzed [15]. The results show that the classification accuracy reaches up to 86.1% under a realistic test environment [15].  
In [16], three methods, including a Convolutional Long Short-term Deep Neural Network (CLDNN), a Long Short-Term Memory neural network (LSTM), and a deep Residual Network (ResNet) have been proposed to recognize ten different modulation types. The results indicate that the classification accuracy is increased by up to 90% at high SNRs.  
Further Principal Component Analysis (PCA) has been deployed to optimize the classification process by minimizing the size of the training dataset [16]. A combination of the transfer learning and a pretrained Inception-ResNetV2 has been presented in [17] to recognize three modulation types, namely Binary Phase Shift Keying (BPSK), QPSK, and 8 PSK at SNR equal to 4 dB. As the results illustrate, the classification accuracies to recognize BPSK, QPSK, and 8 PSK are 100%, 99.66%, and 96.33% respectively [17].  
In [18], a robust hierarchical DNN architecture is presented that performs a hierarchical classification to estimate data type (analog or digital modulation), modulation class, and modulation order. To this purpose, spectrogram snapshots computed from baseband signal in-phase and Quadratic (I/Q) components of the signal are used as the input of the CNN and reach out the performance of 90% at high SNR for most modulation schemes [18]. [PERSON] et al. present an efficient methodology using CNN and Recurrent Neural Networks (RNN) to classify six modulation types under two-channel distortions such as Additive White Gaussian noise (AWGN) and Rayleigh fading [19]. According to the experimental results, the classification precision of the CNN is always close to 100% in AWGN channel [19].  
Even in the Rayleigh channel, the minimum classification accuracy still approaches 84%, whereas the maximum value is near 96%. Ref. [20] proposes a robust CNN-based approach that can precisely classify four types of modulation, including BPSK, QPSK, 8 PSK, and 16 QAM in an orthogonal frequency division multiplexing (OFDM) system under the presence of Phase offset (PO). In [21], CNN and LSTM have been used to solve the AMC problem.  
Furthermore, the proposed classifiers that are based on the fusion model in serial and parallel modes are highly beneficial to improving classification accuracy when the SNR is ranging from 0 dB to 20 dB [21]. As is shown, the serial fusion mode has the best performance compared with other modes.