## 4 Results
### Classification Accuracy  
The CV-CNN-TCN and CV-CNN-TCN-DCC architectures outperform other models, such as CLDNN, SCRNN, ResNet, and CNN, across all SNR conditions, with the performance gap being most significant in low-SNR environments. At SNR = \(-\)10 dB, the CV-CNN-TCN achieves an accuracy of 37%, while the CV-CNN-TCN-DCC further improves to 40%. In contrast, the next-best-performing model, ResNet, only reaches 33%, and simpler models like SCRNN and CLDNN fall below 30%. This advantage becomes more pronounced when comparing metrics like the F1-score and MCC, where CV-CNN-TCN-DCC achieves 0.38 and 0.37, respectively, at SNR = \(-\)10 dB, compared to ResNet's 0.34 and 0.33. These results, illustrated in Figure 5, emphasize that while traditional models struggle to generalize in noisy conditions, the inclusion of temporal and dilated causal convolutions in CV-CNN-TCN-DCC strengthens feature extraction and temporal representation. At higher SNRs, the performance gap narrows, with most models converging to high accuracy. For example, at SNR = 10 dB, the CV-CNN-TCN-DCC achieves 75% accuracy, marginally higher than ResNet's 73% and CNN's 72%. Metrics like the Jaccard index and MCC further highlight the superiority of the CV-CNN-TCN-DCC architecture, achieving values of 0.58 and 0.67 at SNR = 0 dB, compared to 0.55 and 0.64 for ResNet and 0.53 and 0.61 for CNN. While simpler models like SCRNN and CLDNN exhibit competitive performance under high SNR conditions, their inability to extract hierarchical features under noise limits their application in real-world scenarios. The results, summarized in Figure 6, demonstrate the significant advantage of integrating advanced architectures like DCC, particularly for low-SNR environments, where robustness and stability are critical. At SNR = \(-\)10 dB, CV-CNN-TCN-DCC achieves a negative predictive value (NPV) of 93.4% and specificity of 93.5%, outperforming its predecessor, CV-CNN-TCN, which attains 92.6% and 92.9%, respectively. Other models, such as ResNet (91.8% NPV) and SCRNN (90.7% NPV), demonstrate comparatively lower values. The integration of dilated causal convolutions in CV-CNN-TCN-DCC proves effective in improving prediction reliability and mitigating false negatives, which is critical in noisy environments. At moderate and high SNR levels, all models exhibit closer performance, yet CV-CNN-TCN-DCC consistently maintains a slight edge. For example, at SNR = 10 dB, CV-CNN-TCN-DCC reaches an NPV of 97.6% and a sensing error rate of 5.6%, outperforming ResNet, which achieves a sensing error of 6.8%. The false discovery rate (FDR) of CV-CNN-TCN-DCC is also the lowest among all models, declining to 20.1% at SNR = \(-\)5 dB, compared to 22.4% for ResNet and 24.3% for SCRNN. Figure 4: Comparison between (**a**) training accuracy and (**b**) training loss of different models in the RadioML2016.10b dataset. The results presented in Figure 7 analyze the impact of activation functions, batch sizes, and the number of training epochs on the performance of the proposed model, CV-CNN-TCN-DCC, in terms of classification accuracy across varying SNR levels. Figure 7a evaluates different activation functions: ReLU, ELU, LReLU, and SELU achieves the highest accuracy at low SNR conditions (e.g., 78.2% at \(-\)10 dB) and maintains competitive performance across all SNR levels, indicating its ability to handle nonlinearities effectively in noisy environments. LReLU and ReLU follow closely, with ELU showing slightly inferior results. Figure 7b illustrates the effect of batch size, where a batch size of 256 provides a balanced performance, achieving 77.6% accuracy at \(-\)10 dB and converging faster in high SNR ranges compared to other configurations. Smaller batch sizes (128) show slightly improved results under extremely low SNRs, while larger batch sizes (1024) perform suboptimally in these conditions. Finally, Figure 7c examines the number of epochs.