# Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
## 2 Related Work  
In recent years, several studies have focused on a modulation recognition method based on CNN. The first neural network for automatic modulation recognition (AMR) was designed by [PERSON] in 2016 [9]. The CNN [10] at once became the realization paradigm for AMR. After that, scholars conducted extensive research on the neural network method for AMR and designed many models to achieve the accurate recognition of modulated signals, such as long short-term memory (LSTM) [11], RseNet [12], and the convolutional long short-term memory deep neural network (CLDNN) [13]. There is no doubt that the neural network model has become an effective method for AMR. Inspired by the AlexNet model, [PERSON] et al. found that removing the full connection layer had little effect on the results [14]. They used a combination of three layers of CNN and pooled the layers, with SoftMax as an activation function, and achieved good performance. One study [15] proposed an end-to-end CNN-based automatic modulation classification (CNN-AMC) that improves accuracy through step-by-step training and improves the training speed by introducing migration learning. In [16], the authors proposed a heterogeneous depth network model based on CNN-BiLSTM, which combines CNN's local feature with LSTM's time characteristic. The model adopted five layers of CNN and two layers of BiLSTM (serially and in parallel). In [17], the modes are optimized, based on AlexNet, retaining the original reel layer, optimizing the parameters of the full connection layer and the pooling layer, and reducing the number of neurons. The training speed was improved and the risk of overfitting was avoided. The authors of [18] proposed deep neural networks (DNN) and a receptive field block net (RFBN), based on classification characteristics, and compared the classification performance of multiple input multiple output (MIMO) wireless modulation with K-NN, AdaBoost, and CNN networks. The authors of [19] converted complex signals into data formats (such as images) with a grid topology, as in its original application for graphics recognition, and tried to use AlexNetand GoogleNet for modulation recognition. In [20], a 34-layer CNN was designed that deepened the number of layers, showed better recognition accuracy for low signal-to-noise ratio modulation signals, and avoided under-sampling and oversampling. The authors of [21] designed a model that combined semi-supervised learning (SSL) and CNN, taking the advantages of CNN, with its high applicability, and the anti-jamming capability of SSL, with convenience and faster training speed. The authors of [22] designed a deep learning network based on a layered sparse self-encoder and SoftMax regression, which provided some performance improvement over traditional recognition methods. With the increase in complexity of the model, the computational load became very large. In [7], the authors designed a LightAMC model that made the scaling factor sparse by introducing the scale factor into the neurons and compressive sensing to help prune redundant neurons. The model size of this method was reduced by 93.5% and the calculation speed was accelerated, with a small performance loss. The authors of [23] proposed an average percentage of zeroes pruning method to reduce the network size by 37.16%, at the cost of slightly reducing the classification accuracy. The above-mentioned works are mainly focused on research into the CNN network, and are less focused on the RNN network; some networks have too many layers, which necessitates a long training time. Thus, a model is proposed that consists of only two layers of a CNN network and two layers of an LSTM network, which represents fewer layers and less training time; we have tested it to maintain high accuracy with less training time. In the previous works, most of them pruned the connection weights or neurons, that is, fine-grained pruning. When the weight reconnection is pruned, the network structure will become unstable, which is difficult to address in practice.