## 3 Results and Discussion
When we divide the training instances among multiple working nodes, a bigger batch size enables us to calculate more in parallel. Model training can be greatly accelerated by doing this. Figure 8 demonstrates that the optimal performance is achieved when the batch size is 128. Large batches converge to sharp minimization, which varies substantially, while training with small batches tends to converge to flat minimization, which varies very minimally in the narrow neighborhood of minimization. Operational processing becomes slower than before when batch size increases, because batch size increases also result in a significant rise in time. Through the previous experimental analysis, to determine the model parameters, we sought to assess the correlation between the iterative connection depicted in Figure 9 and the accuracy of the training loss value. As the number of iterations rose, the identification  
Figure 6: Classification accuracy under different GRUs layers. Figure 7: Classification accuracy under different numbers of GRU units. accuracy improved, and the function's loss value steadily decreased, suggesting that the model's convergence was adequate. Figure 10 displayed the identification accuracy of 10 modulation signals under different SNRs. The maximum recognition accuracy was 93.79% when the SNR was at 18 dB. All signals except WBFM were properly detected, and the recognition accuracy was above 97% when the SNR was greater than 5 dB. The average recognition accuracy was higher than 90% when the SNR was greater than 0 dB. The difficulty is exacerbated by the fact that the WBFM signal in the dataset is produced by sampling analog audio sources, and there are silence cycles. The greatest recognition performance, the strongest anti-interference capacity against noise, and 100% accurate identification were all exhibited by GFSK and CPFSK. The outcome suggests that the technique may be applied for more accurate modulation recognition. With 100% accurate identification, the greatest recognition performance, and the highest noise-cancelling capabilities, GFSK and CPFSK were the most effective. The results indicate that the method can be used for modulation recognition with higher accuracy. Furthermore, the network model presented in this research was sufficiently extended to be used for the problem of modulated signal identification. We conducted a comparison between the suggested strategy and eight popular network frameworks in order to determine its effectiveness. All of the aforementioned classifications made use of the RadioML2016.10b datasets and were balanced. The majority of the suggested method's recognition accuracy rates display a leading level, as seen in Figure 11. The recognition  
Figure 8: Effect of different sizes of batch size on identification. Figure 9: Relationship curve between number of iterations, accuracy, and loss value. accuracy rate has been consistently higher than 93% when SNR is higher than 5 dB. In comparison to other models, the DAE, RESNET, and MCNET models have poorer recognition accuracy. At 6 dB, DENSENET has the highest recognition rate of all the comparison models; however, at greater SNR, the recognition accuracy keeps decreasing. This is because the model has a lot of parameters, which leads to overfitting and lowers recognition accuracy. The model presented in this paper shows good performance at all signal-to-noise ratios. Similarly, as a lightweight index, Figure 12 shows that the proposed method uses the minimum parameter compared with the reported methods, and thus can decrease computational complexity 50% or more. This shows that the proposed method increases more training groups to improve high classification accuracy, and average recognition accuracy is also at a high level.