## 3 Simulation Experiments and Analysis
### Model Performance Analysis
In this scenario, the loss and accuracy exhibit noticeable fluctuations. However, as it progresses, the convergence speed gradually accelerates, and the accuracy improves to some extent. Under the 5-way 5-shot condition, the loss quickly converges to a small value, and the accuracy simultaneously improves at the same time, with smoother fluctuations in both metrics. This indicates that the model still possesses some recognition capability under 0 dB SNR, although its performance may not be as strong as in higher SNR conditions. Under 6 dB SNR, the convergence speed further improves, with the loss decreasing at a faster rate and stabilizing more quickly. This indicates that in higher SNR conditions, the model can learn effective features more rapidly, thereby enhancing its classification performance. Under both the 5-way 1-shot and 5-way 5-shot conditions, the model exhibits improved convergence speed and accuracy. However, the performance of the 5-way 5-shot setup is relatively superior, possibly since it utilizes more samples during each iteration, allowing the model to capture the differences between categories more effectively. Under 18 dB SNR, the energy of the signal is relatively larger compared to the noise, resulting in a clearer distinction between the signal and noise. Consequently, the model can more accurately extract the signal's features and identify the modulation types. Whether under the 5-way 1-shot or 5-way 5-shot conditions, the loss rapidly decreases, and the convergence speed is significantly faster than that observed at \(-4\) dB and 0 dB. Additionally, the accuracy reaches a higher level within a short time and exhibits less fluctuation, indicating improved stability of the model at higher SNR. Furthermore, due to the clearer distinction between the signal and noise, the fluctuations in loss and accuracy become smoother, indicating increased stability of the model in this scenario. The examination of the four subplots in each graph, organized according to SNR, highlights a noticeable trend. The model's performance exhibits enhanced robustness with varying SNR levels. This phenomenon is attributable to SNR, which quantifies the proportion between signal and noise. A higher SNR denotes a more pronounced signal in relation to noise, whereas a lower SNR indicates a relatively stronger noise presence. In conditions of elevated SNR, signal degradation is minimal, and noise's impact on the signal is limited. This prominence accentuates the manifestation of modulation pattern characteristics, thereby offering an amplified dataset for analysis during recognition. This reduction in susceptibility to noise interference ultimately stabilizes the model's decision boundaries, facilitating clearer differentiation between distinct modulation patterns and augmenting recognition accuracy. Conversely, under lower SNR, the signal may be engulfed by noise, obscuring its features and providing unreliable data for analysis and learning. This hinders precise identification and separation, complicating pattern discrimination. It is noteworthy that the performance enhancement between 18 dB SNR and 6 dB SNR is marginal. At 6 dB SNR, the model has already garnered adequate information for proficiently classifying signal modulation patterns. Furthermore, a juxtaposition of the two plots on the left and right unveils that, in terms of convergence speed and final accuracy post-convergence, the 5-way 5-shot condition generally outperforms the 5-way 1-shot counterpart. This difference emanates from the fact that the 5-way 5-shot scenario incorporates five samples per category, whereas the 5-way 1-shot condition encompasses merely one sample per category. This disparity stems from the understanding that an augmented pool of training samples typically aids the model in superiorly capturing features and patterns. The 5-way 5-shot condition presents a more substantial training dataset, facilitating more precise feature comprehension within each category. This, in turn, enhances the model's adaptability to new tasks.