# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning
### Rnn  
RNN was first proposed by [PERSON] in 1990 [PERSON] (1990). It takes sequence data as input and recurses in the evolution direction of sequence. All nodes are connected by chain. Unlike other feedforward machine learning algorithms, data flow only flows in one direction, that is, input to output. As shown in Figure 2, a cyclic neural network diagram is composed of input layer, cyclic layer, fully connected layer and output layer. \(X(t)\) refers to the input vector, \(O(t)\) refers to the output vector, \(H(t)\) refers to the weight matrix from the hidden layer to the fully connected layer at the current time and \(H(t\)-1) refers to the weight matrix from the hidden layer to the fully connected layer at the previous time. The cyclic layer is used for feature extraction and the fully connected layer is used for feature classification. It is a network with memory. Because it contains something like memory inside, the current state depends not only on the input of the current moment, but also on the input of the previous moment. This looks like the circulatory unit in RNN neurons. Expanding the neurons of RNN circulatory layer along the time sequence is the structure shown in Figure 3, where the grey box is the circulatory unit, \(X(t)\) represents the input vector at a certain time and \(H(t)\) indicates the state of the hidden layer at the current time. Although RNN is theoretically unrestricted in length of time series, it has a long-term dependence problem; that is, when learning a long sequence, in the cyclic neural network will appear gradient vanishing and gradient explosion ([PERSON], 1997) phenomena, unable to grasp the long-span non-linear relationship. To improve long-term dependency, [PERSON] proposed the LSTM in 1997 to improve its cell structure ([PERSON], 1997). As shown in Figure 4, \(X(t)\) represents the input of the LSTM unit and \(H(t)\) represents the output of the LSTM unit. The RNN is endowed with the ability to control its internal information accumulation by adding a gating unit ([PERSON], 1997), so as to control the influence of the input at the current time on the network. More specifically, the LSTM unit contains three  
Figure 3: Cyclic unit ([PERSON], 1997). Figure 2: RNN structure. gates: the input gate, the forgotten gate and the output gate. Input gates allow input signals to adjust the storage unit state or prevent extreme operations such as setting the input door to zero; output gates allow input signal unit states to affect other neurons or prevent similar operations; and forgotten gates enable storage units to remember or forget their previous state, with only a small amount of linear interaction in the information flow. This makes it easier to remember long-term information and the model more easily converges. LTSM can solve the problem of gradient vanishing on the one hand and remember the past data on the other hand. LSTM is trained using back propagation, so time series can also be classified with time lags of unknown duration. There are also many studies in the literature on radio modulation recognition methods based on RNN as summarized in Table 3 and decribed below. [PERSON] et al. [23] proposed a two-layer GRU model with appropriate parameters based on RNN model. Using the time series characteristics of the signal, the original signal can be directly used with limited data length, avoiding manual signal extraction. Compared with [90], the double-layer GRU model has obvious advantages in high SNR. [PERSON] et al. [57] analyzed the effect of CNN layer size and depth on classification accuracy and proposed a complex priori module that combines CNN and LSTM modules to improve classification accuracy of radio AMR. [PERSON] et al. [24] proposed a classification method of radio AMR based on LSTM, learning from the amplitude and phase information of the modulation scheme in the time domain that exists in the training data, without the need for expert features such as high-order cyclic moments. [PERSON] et al. [49] proposed an AMR model based on LSTM.