## 3 Simulation Experiments and Analysis
### Model Performance Analysis  
In this section, we focus on analyzing the loss and accuracy of the model under the two classic conditions of 5-way 5-shot and 5-way 1-shot. Due to the smaller dataset in meta-learning compared to traditional machine learning methods, the loss and accuracy exhibit more pronounced fluctuations. As shown in Figure 4, regardless of the conditions of 5-way 5-shot and 5-way 1-shot, the loss rapidly decreases and converges at the beginning. The loss measures the gap between the model's predicted results and the actual results, and its decrease indicates a gradual improvement in the model's performance. When the loss converges, it means that the model has found a set of parameters that minimizes the loss function, resulting in optimal model performance. At the same time, the accuracy continuously increases, indicating that the model is learning and acquiring knowledge from the data. As it progresses, the model's predictions for the data become increasingly accurate. By comparing the results, it is evident that 5-way 5-shot outperforms 5-way 1-shot. Compared to using only one signal sample to represent each category in 5-way 1-shot, 5-way 5-shot provides more information, enabling the model to converge faster and significantly improve accuracy. This allows the model to better understand and differentiate the differences between different modulation schemes. To further investigate the performance of the model, we conduct separate tests on four representative signal-to-noise ratios (SNRs): \(-4\) dB, 0 dB, 6 dB, and 18 dB. Specifically, we focus on analyzing the loss and accuracy under the 5-way 5-shot and 5-way 1-shot conditions for each SNR. For these individual tests at different SNRs, we delete the weight files and randomly initialize the model parameters. Additionally, since the accuracy in the graph is calculated on the set, the maximum accuracy reaches 1, which is different from the accuracy during testing. The experimental results are shown in Figure 5. \begin{table}
\begin{tabular}{c c} \hline \hline  & **Modulation Type** \\ \hline Train Set & 16 APSK FM GMSK 32 APSK OQPSK 8 PSK AM-SSB-SC 4 ASK 64 QAM 16 PSK 64 APSK 128 QAM AM-SDB-SC AM-DSB-WC 256 QAM OOK 16 QAM \\ Test Set & 32 PSK 32 QAM 8 ASK BPSK 128 APSK QPSK AM-SSB-WC \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dataset partitioning for experiments in this paper. Under \(-4\) dB SNR, the signal energy is relatively small compared to the noise energy, resulting in a mixture of the signal and noise that is difficult to distinguish. As a result, the model is initially trapped in a local optimum, leading to little change in the loss and accuracy for a while. However, the Adam optimizer, which employs adaptive learning rates and incorporates the concept of momentum, dynamically adjusts each parameter and  
Figure 4: Loss and accuracy of the model under the two classic conditions of 5-way 5-shot and 5-way 1-shot. (**a**) Loss and accuracy under the 5-way 1-shot condition; (**b**) loss and accuracy under the 5-way 5-shot condition. Figure 5: Loss and accuracy of the model under four typical signal-to-noise ratios: \(-4\) dB, 0 dB, 6 dB, and 18 dB. (**a**) Loss and accuracy under the 5-way 1-shot condition; (**b**) loss and accuracy under the 5-way 5-shot condition. helps escape local optima. In the case of 5-way 5-shot, the loss starts to decrease, and the accuracy begins to rise, eventually reaching a lower loss value and a higher level of accuracy. This indicates that the model achieves good performance under \(-4\) dB SNR. In the case of 5-way 1-shot, there is greater fluctuation in the loss and accuracy, and the convergence speed is relatively slower. It takes more epochs to escape from the local optimum, but the model is still able to continue converging. In the case of 5-way 5-shot, there is a noticeable improvement in convergence speed. Under 0 dB SNR, the distinction between the signal and noise is relatively improved compared to \(-4\) dB but still poses certain challenges.