## 1 Introduction
Deep learning has developed rapidly and widely been applied in computer vision [18], speech recognition [19], natural language processing [20], etc. While widely applied in various domains, deep learning has also grown tremendously. In addition to CNN, Long Short-Term Memory (LSTM) network [21], Residual Neural Network (ResNet) [22] and many other deep learning structures are developed for different problems. Deep neural networks put feature extraction and classifier construction together, they are trained simultaneously. Deep neural networks are trained automatically to find the most suitable features and the corresponding classifier for the input dataset; hence, deep learning algorithms can obtain more expressive features than handcraft features. Therefore, deep neural networks have been more and more popular in AMC domain. Deep learning methods consist of two categories depending on the input of neural networks is raw data or not. The input of Feed-forwards neural network (FNN) and CNN can be features extracted from traditional machine learning methods. Ref. [23] calculated amplitude variance, maximum value of the power spectral density of the normalized centered-instantaneous amplitude, in-band spectral variation, high order statistics and deviation from unit circle as input features, the classifier is a designed artificial neural network. Ref. [24] extracted High-Order Cumulant features, the standard deviation of the absolute value of the non-linear component of the instantaneous phase and the peak to average radio as features, the classifier is a designed 4-layer artificial neural network. Ref. [24] can recognize modulation types under fading channels. Some literatures mapped the input IQ signal to some types of images, and then trained them by CNN designed from computer vision domain. Ref. [25] mapped the IQ signal to constellation images and applied AlexNet and GoogleNet from computer vision domain for classification. Ref. [26] mapped the input signal to ambiguity function images and utilized stacked sparse auto-encoder to extract features, the classifier is a two-layer neural network. Ref. [27] transformed very high frequency radio signals to cyclic spectrum images and classified by CNN and the accuarcy reached 95% for an SNR or 2 dB. However, transformations of the raw signal will Inevitably result in a part of information loss and the deep learning methods have ability to handle raw signals. Thus, the second category is to process the raw data directly. [PERSON] [28] first provided the standard dataset and the baseline network of AMC. Deep Belief Network [29] has been applied in AMC early but shown lower accuracy than conventional discriminant model. [PERSON] [30] projected the amplitude and phase of each samples and put the transformed signal into a 2-layer LSTM network for classification and exceeded more than 80% accuracy for an SNR of 10 dB under [PERSON] dataset. [PERSON]. [31] applied GRU for AMC with resource-constrained end-devices. [PERSON] [32] designed a LSTM-based denoising autoencoder classifier and exceeded 90% for an SNR of 4 dB. [PERSON] [33] utilized Inception structure [34] and Residual module to extract features and can reach 80% accuracy for an SNR of 0 dB. [PERSON] [35] proposed a Convolutional Long Short-term Deep Neural Network (CLDNN) which cascaded CNN and LSTM. [PERSON] [36] proposed an CNN-based method adding Gaussian noise layer and performed better than baseline. Dropout-replaced Convolutional Neural Network (DrCNN) [37] was proposed which replaced max pooling layer with dropout layer [38] and obtain a competitive result. [PERSON] [39] combined ResNet and GRU and reached 95% for an SNR of 5 dB. [PERSON] [40] proposed sequential convolutional recurrent neural network which connected CNN and bi-LSTM in series and got a competitive result. However, serial structure of CNN and RNN aliases different types of features, which will result in the loss of information inevitably. Thus, parallel structure with a proper feature combination method can reach a better performance.