# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 5 Conclusions  
This paper proposes an improved spatiotemporal multi-channel network model (IQGMCL) based on the existing modulation signal recognition algorithms. The first chapter analyzes the research status at home and abroad in recent years and summarizes the existing advanced signal modulation identification networks. Chapter 2 describes the three open-source signal modulation identification datasets used in this article. Chapter 3 introduces the various improved modules in IQGMCL, which theoretically justifies the improvements. Referring to the multi-channel data input form of the MCLDNN network, the structure diagram of the IQGMCL model is drawn by fusing each module. Chapter 4 includes three parts: ablation experiment, comparative test, and experimental conclusion. The ablation experiment mainly determines the improved network structure quantitatively to achieve a better signal recognition effect for the increase in training features. The comparative experiment part mainly compares the IQGMCL structure determined in the ablation experiment with various existing advanced algorithms. It is found that the IQGMCL network has better recognition accuracy on three datasets. Among the comparison networks, it can be found that the MCLDNN network has the best recognition accuracy. Therefore, MCLDNN is used as a reference object for further experiments. The comparison curves of Train_accuracy, Train_loss, Val_accuracy, and Val_loss of IQGMCL and MCLDNN networks on three datasets are plotted, drawing the Val_accuracy and Val_loss comparison bar charts of the two networks under the optimal case. From the results, IQGMCL has higher Train_accuracy and Val_accuracy, lower Val_accuracy and Val_loss, and IQGMCL networks converge faster. Finally, by showing a part of the confusion matrices, the recognition effect of IQGMCL and MCLDNN is compared more directly. The experimental conclusion summarizes the advantages of IQGMCL network recognition accuracy and illustrates the progressiveness of the IQGMCL network in the current modulation recognition field. The innovation points of the improved spatiotemporal multi-channel network proposed in this paper are as follows:  
1. Adding the relevant features of the IQ signal to the original MCLDNN network data input. After introducing the related features of IQ signals, drawing on the idea of a small convolution kernel in the VGG network, a small convolution kernel (1,3) is used to extract time series features further. These increase the number of model training features and improve the training accuracy and verification accuracy of the model. 2. Due to the increase in training features, when the features are extracted by the convolutional layer and the data need to be further processed, it is necessary to quantitatively redesign the network structure on the basis of the MCLDNN network. Specifically, introducing Bi-LSTM to replace LSTM. On the basis of one-way flow from the past to the future of the LSTM network, increasing data flow from the future to the past. Bi-LSTM can better explore the timing characteristics of data and improve the robustness of the model. Then, on the basis of the MCLDNN network, removing a full-connection layer. It can not only prevent overfitting but also reduce the model training parameters and also speed up the convergence of the model. 3. Multiplicative Gaussian noise is introduced before the classification after the full connection layer. It corrodes the features randomly, effectively alleviates the problem of training overfitting, improves the robustness of the model, and improves the generalization ability of the network. In this experiment, three open-source datasets are used for comparative experiments, which verifies the universal adaptability of the model and is convincing. Combine the experimental results and conclusions to clarify the next step:  
1. It can be concluded from the experimental results that, no matter whether it is in the RML2016.10a or RML2016.10b dataset, it is not easy to improve the recognition accuracy of signals with high SNR.