# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 1 Introduction  
In recent years, the role of communication technology in war application has become more and more apparent. Satellite communication technologies are particularly important [1; 2]. Among them, modulation mode recognition has an essential application in information warfare. Signal modulation is an important technology in modern communication systems [3]. The so-called signal modulation is a process of loading the low-frequency signal carrying the information to be transmitted onto the high-frequency signal. It is convenient for information propagation in an electromagnetic environment. However, in modern wireless communication, IQ signal modulation is standard. IQ signal is also called quadrature signal in the same direction [4]. I by in-phase(in-phase), Q by quadrature(orthogonal), and the phase difference between Q and I is 90 degrees. IQ signal is the mapping of continuous signal in a two-dimensional rectangular coordinate system, usually used for baseband signal conversion and reconstruction, and automatic modulation classification recognition plays an important role in modern wireless communication. With the popularity of artificial intelligence technology, deep learning is applied more and more in modulation recognition algorithms [5]. Especially convolutional neural network has become a hot research topic. Traditional pattern recognition methods [6; 7] require a lot of manual calculations when extracting the features of samples, and the recognition accuracy of multi-class modulation signals is not ideal. However, the neural network can automatically learn sample features from massive samples rather than manually designing expert features [8; 9]. In 2016, [PERSON] introduced a convolutional neural network (CNN) into the field of modulation signal recognition for the first time. When the SNR was greater than 0 dB, the average recognition accuracy reached 78% on the RML2016.10a dataset. In the same year, the CNN1 network [10] was proposed. On the RML2016.10a dataset, the recognition accuracy can reach 81%. In 2017, [PERSON] and [PERSON] applied the CLDNN network model, which plays an outstanding role in the field of speech recognition, to signal modulation identification [11], and the recognition accuracy reached 83% on the publicly available RML2016.10a dataset. In the same year, [PERSON] proposed the ResNet network, DenseNet network, and CLDNN2 network [12]. These three networks achieved the highest recognition accuracy of 83.5%, 86.6%, and 88.5%, respectively, in the dataset containing ten types of modulated signals generated by GNURadio. In 2020, [PERSON] proposed the CNN2 network [13]. This network increased the number of convolution layers and the number of hidden nodes on the basis of CNN1, and the identification accuracy reached 84% on the RML2016.10a dataset. At the same time, a new dataset was introduced for testing. The experiment showed that this network had better recognition performance on the HisarMod2019.1 dataset than RML2016.10a. The same year, [PERSON] proposed the IC-AMCNet network [14]. Compared with the existing CNN architectures, it adjusts the number of layers and adds new layer types to meet the estimated delay standard beyond the fifth generation (B5G) communication. According to the simulation results, the proposed scheme has a better advantage in terms of calculation time, and the recognition accuracy reaches 84% on the RML2016.10a dataset and 92.6% on the RML2016.10b dataset. In 2021, [PERSON] proposed the CGDNet network [15] and introduced Guassian-Dropout, which enhanced the feature extraction process and prevented the problem of gradient disappearance. In the same year, [PERSON] proposed a PET-CGDNN network based on phase parameter estimation and transformation [16]. Compared with the CLDNN network and CLDNN2 network, this network uses a convolutional neural network (CNN) and gated cycle unit (GRU) as the feature extraction layer, which maintains a recognition accuracy rate higher than 90%. The number of parameters is less than 1/8.