# Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
###### Abstract  
With the development of communication technology and the increasingly complex wireless communication channel environment, the requirements for radio modulation recognition are also increased to avoid interference and improve the efficiency of radio spectrum resources. To achieve high recognition accuracy with less computational overload, we propose a radio signal modulation recognition method based on deep learning, which uses a pruning strategy to reduce computational overload, based on the original model, CNN-LSTM-DNN (CLDNN), and the double-layer long short-term memory (LSTM). Effect factors are analyzed in terms of recognition accuracy by adjusting the parameters of each network layer. The results of the experiments show that the model not only has a greater precision improvement than some existing models, but also reduces the computational resources necessary.  
modulation recognition; deep learning; pruning +
Footnote †: journal: Applied Sciences  
learning technology that some researchers have tried to use for radio signal characteristics extraction and recognition. However, there are still problems, such as fitting and optimizing algorithms for the recognition of modulation methods in the field of radio. There are two limitations of using the convolutional neural network (CNN) model for radio modulation recognition. Firstly, if the number of network layers increases, but an improvement in recognition accuracy is not obvious, the training time load will increase [6]. Secondly, the input radio signal is a time-dependent sequence, while the image is a two-dimensional matrix, so the traditional CNN method for image recognition cannot achieve a good feature extraction effect in wireless modulation recognition. Moreover, the traditional CNN may contain many neurons or parameters that do not contribute greatly to the results. These redundant neurons may slow down the training and recognition of neural networks and may take up unnecessary hardware resources [7]. Therefore, to make our model smaller and more efficient, a pruning strategy needs to be adopted to reduce the training costs. This is particularly important for applications with a high level of real-time performance, such as radio modulation recognition, and in devices with weak computing power, such as mobile devices and edge devices.  
In this paper, a pruning strategy for CLDNN [8] is proposed that greatly reduces the complexity of the model by removing unnecessary neuron nodes from the original model. The proposed method effectively improves the efficiency of radio signal modulation and reduces the risk of overfitting. Moreover, a double-layer LSTM layer is used by considering the time characteristics of the radio signal to further improve the accuracy of the radio signal modulation mode recognition. The experimental results show that the model achieves a greater precision improvement than the original model.  
The rest of this paper is organized as follows. In Section 2, some related works in the literature are discussed. In Sections 3 and 4, the analysis model and our proposed method are given. The simulation research and performance evaluation of the proposed method is presented in Section 5. Finally, the concluding remarks and future work are presented.