# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 2 Dataset Introduction  
This paper adopts three open-source modulation recognition datasets [10], which are RML2016.10a, RML2016.10b, and RML2016.04c. Three datasets are generated by [PERSON] and [PERSON] using the GNURadio software platform. RML2016.10a and RML2016.04c datasets include three types of digital modulation signals and eight types of analog modulation signals. RML2016.10b dataset includes three types of digital modulation signals and seven types of analog modulation signals. The SNR of various modulated signals ranges from \(-\)20 dB to 18 dB, with an interval of 2 dB. There are 20 kinds of SNRs in total. The size of each modulated signal is (2, 128); 128 corresponds to 128 sampling points per signal, and 2 corresponds to the input signal being a quadrature IQ two-way signal [19; 20; 21]. The RML2016.10a dataset has a total of 220,000 signals, the RML2016.10b dataset has a total of 1,200,000 signals, and the RML2016.04c dataset has a total of 162,060 signals. When preprocessing the dataset, the training set, validation set, and test set are divided according to the ratio of 6:2:2. In the specific division, random proportional sampling is carried out according to the number of samples of each modulation signal at each SNR. Taking the RML2016.10a dataset as an example, the number of each signal at each SNR is 1000. Randomly take 600 out of 1000 and put them in the training set, put 200 randomly into the validation set, and put the remaining 200 into the test set, so the dataset is organized in this way. Finally, the training set shape is [132000, 2,128]. The verification set shape is [44000, 2, 128]. The test set shape is [44000, 2, 128]. Similarly, in RML2016.10b, the dataset is also organized in this way. Finally, the training set shape is [720000, 2, 128]. The verification set shape is [240000, 2, 128]. The test set shape is [240000, 2, 128]. In RML2016.04c, the training set shape is [97160, 2, 128]. The verification set shape is [32460, 2, 128]. The test set shape is [32460, 2, 128]. The network training in this paper is supervised learning. Each sample signal has a corresponding data label, which corresponds to the category name of each signal. The dataset labeling process adopts the one-hot encoding method. Take the RML2016.10a dataset as an example. The dataset includes 11 types of modulated signals. The category of each signal can be obtained from the dataset. If a signal belongs to the first category, its one-hot encoding method is [1,0,0,0,0,0,0,0,0,0,0], and so on. Finally, The training set label shape is [132000, 11]. The verification set label shape is [44000, 11]. The test set label shape is [44000, 11]. Similarly, in RML2016.10b, the training set label shape is [720000, 10]. The verification set label shape is [240000, 10]. The verification set label shape is [240000, 10]. In RML2016.04c, the training set label shape is [97160, 11]. The verification set label shape is [32460, 11]. The three simulation datasets are highly similar to the real signals, so extending the model to the real signal datasets is of great significance in conducting experiments on these datasets. Table 1 describes the datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset Name** & **Modulation Schemes** & **Sample Dimension** & **Dataset Size** & **SNR Range (dB)** \\ \hline RML & 11 classes (SPSK, BPSK, CPFSK, GFSK, PAM, AM-DSB, 2016.10a & AM-SSB, 16 QAM, 64 QAM, QPSK, WBFM) & 2 \(\times\) 128 & 220,000 & \(-\)20:2:18 \\ \hline RML & 10 classes (SPSK, BPSK, CPFSK, GFSK, PAM, AM-DSB, 2016.10b & 16 QAM, 64 QAM, QPSK, WBFM) & 2 \(\times\) 128 & 1,200,000 & \(-\)20:2:18 \\ \hline RML & 11 classes (SPSK, BPSK, CPFSK, GFSK, PAM, AM-DSB, 2016.04c & AM-SSB, 16 QAM, 64 QAM, QPSK, WBFM) & 2 \(\times\) 128 & 162,060 & \(-\)20:2:18 \\ \hline \end{tabular}
\end{table} 
Table 1: Open-source modulation recognition dataset description [10; 22; 23; 24]. The RML2016.10a dataset is taken as an example.