## 4 Results
### Recognition Accuracy by Modulation and SNR  
Tables 4-6 illustrate the recognition accuracy of various modulation types at SNR levels of \(-4\) dB, \(0\) dB, and \(4\) dB, comparing baseline models (CNN, ResNet, SCRNN, and CLDNN) against our proposed models CV-CNN-TCN and CV-CNN-TCN-DCC.  
* At \(-4\) dB SNR (Table 4): Our proposed CV-CNN-TCN and CV-CNN-TCN-DCC models outperform baselines for most modulation types, particularly for 16-QAM, 64-QAM, and WBFM, where baseline models show significant drops. For instance, CV-CNN-TCN-DCC achieves 49.35% for 16-QAM versus 10.6% (CLDNN).  
At \(0\) dB SNR (Table 5): The proposed models demonstrate significant gains in low-SNR conditions, with CV-CNN-TCN-DCC excelling in 16-QAM (38.14%) and WBFM (76.52%), far surpassing baseline performances. It also maintains high accuracy for complex modulations like BPSK and GFSK, matching or exceeding other temporal models.  
At \(4\) dB SNR (Table 6): The improvements are more pronounced at higher SNR levels. CV-CNN-TCN-DCC achieves superior performance across all modulations, particularly excelling at BPSK (98.04%), 64-QAM (69.68%), and QPSK (74.58%). Compared to baselines like SCRNN and CLDNN, our models provide significant robustness in handling complex modulations and noise resilience.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Modulation** & **CNN** & **ResNet** & **SCRNN** & **CLDNN** & **CV-CNN-TCN** & **CV-CNN-TCN-DCC** \\ \hline
8 PSK & 37.31\% & 23.85\% & 43.27\% & 16.5\% & 20.16\% & 23.23\% \\ AM-DSB & 90.06\% & 78.93\% & 96.56\% & 88.27\% & 91.75\% & 55.25\% \\ BPSK & 37.54\% & 57.41\% & 49.5\% & 55.08\% & 39.72\% & 60.10\% \\ CPFSK & 94.37\% & 62.6\% & 82.06\% & 87.27\% & 85.97\% & 77.50\% \\ GFSK & 94.9\% & 84.87\% & 87.81\% & 91.68\% & 90.12\% & 91.50\% \\ PAM4 & 57.4\% & 85.39\% & 39.14\% & 31.14\% & 93.70\% & 90.89\% \\
16-QAM & 11.54\% & 19.77\% & 6.12\% & 10.6\% & 42.08\% & 49.35\% \\
64-QAM & 61.1\% & 67.4\% & 53.64\% & 39.52\% & 69.04\% & 60.20\% \\ QPSK & 30.5\% & 18.72\% & 22.54\% & 45.54\% & 22.85\% & 31.83\% \\ WBFM & 37.54\% & 35.06\% & 27.41\% & 37.31\% & 30.47\% & 59.97\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition accuracy at \(-4\) dB SNR.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Size (\%)** & **512** & **256** & **128** & **64** & **32** & **16** \\ \hline Classification time (ms) & 19.5 & 11.5 & 6.15 & 4.2 & 3.2 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time complexity of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Modulation** & **CNN** & **ResNet** & **SCRNN** & **CLDNN** & **CV-CNN-TCN** & **CV-CNN-TCN-DCC** \\ \hline
8 PSK & 55.72\% & 43.18\% & 44.41\% & 31.85\% & 66.35\% & 70.68\% \\ AM-DSB & 96.52\% & 83.95\% & 99.16\% & 95.93\% & 99.02\% & 47.37 \% \\ BPSK & 80.68\% & 85.5\% & 73.58\% & 85.75\% & 85.58\% & 92.75\% \\ CPFSK & 99.79\% & 83.62\% & 96.37\% & 98.66\% & 95.12\% & 92.27\% \\ GFSK & 99.31\% & 97.33\% & 98.2\% & 98.29\% & 97.41\% & 92.27\% \\ PAM4 & 93.72\% & 94.81\% & 84.04\% & 85.00\% & 98.41\% & 98.04\% \\
16-QAM & 23.95\% & 19.02\% & 17.54\% & 35.68\% & 30.85\% & 38.14\% \\
64-QAM & 77.12\% & 70.18\% & 67.93\% & 59.14\% & 75.95\% & 67.64\% \\ QPSK & 52.62\% & 55.31\% & 51.62\% & 65.7\% & 62.74\% & 60.66\% \\ WBFM & 39.77\% & 41.02\% & 34.33\% & 38.12\% & 32.48\% & 76.52 \% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Recognition accuracy at \(0\) dB SNR.  
In summary, the proposed model consistently outperforms baseline models, particularly in complex modulations (e.g., 16-QAM and 64-QAM) and lower SNRs.