## 5 Experiments
### Model Recognition Accuracy Evaluation
Nevertheless, the results indicate that the image-based deep learning approach considerably surpasses the IQ-based approach. The CDRNN model achieves a classification accuracy of approximately 92.1% at 18 dB and significantly outperforms the other networks when the signal-to-noise ratio is greater than or equal to 0 dB. Even at SNR less than 0 dB, the scheme shows a slight improvement in performance. The VViT and MobileNet model is less accurate than the CDRNN model in the range of 0 to 18 dB, but shows a significant decline in performance as the SNR decreases. At less than 0 dB, the model of ResNet50 with the addition of the CA mechanism presents better performance than VViT. In terms of accuracy at all SNRs, the CDRNN-based scheme outperforms ResNet50, TFA-SCNN, STFT-CDSC, MobileNet and VViT by 2.28-15%, 0-11.09%, 3.81-15.18%, 1.72-9.18% and 1.66-8.27%, respectively. Thus, the proposed scheme is robust to the modulation type and maintains the highest recognition accuracy even under unfavorable conditions with low SNR. Table 3 reflects a more intuitive performance of the network at different SNRs. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model/Reference** & **Dataset** & \begin{tabular}{c} **Accuracy** \\ **(0 dB)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **(6 dB)** \\ \end{tabular} & \begin{tabular}{c} **Accuracy** \\ **(12 dB)** \\ \end{tabular} & \
\begin{tabular}{c} **Accuracy** \\ **(18 dB)** \\ \end{tabular} &
\begin{tabular}{c} **Mean Value** \\ **(18 dB)** \\ \end{tabular} \\ \hline
**Proposed CDRNN** & **TFI** & **52.82\%** & **70.36\%** & **81.09\%** & **92.09\%** & **74.09\%** \\ AlexNet [13] & TFI & 47.36\% & 59.55\% & 68.55\% & 76.45\% & 62.98\% \\ ResNet50 [14] & TFI & 46.6\% & 60.73\% & 70.09\% & 77.09\% & 63.63\% \\ MobileNet [16] & TFI & 52.09\% & 63.82\% & 74.91\% & 82.91\% & 68.43\% \\ TFA-SCNN [24] & TFI & 52.82\% & 63.91\% & 74.9 & 81\% & 67.96\% \\ STFT-CDSC [28] & TFI & 41.36\% & 53.45\% & 68.18\% & 76.91\% & 59.98\% \\ VViT [29] & TFI & 52.69\% & 64.5\% & 75.91\% & 83.82\% & 69.23\% \\ CNN-IQ [7] & I/Q & 44\% & 57.91\% & 65\% & 73.27\% & 60.05\% \\ AWN [20] & I/Q & 46.91\% & 66.91\% & 73\% & 81.64\% & 67.12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the accuracy between the proposed work and previous works (\(\geq\)0 dB). Figure 7: Accuracy of AMR schemes in fading channel over various SNRs. Under the channel model in Section 3.2, simulation experiments were carried out to make a comparison of the recognition accuracy of different DNNs. Figure 8 presents the relationship between modulation type and SNR. For QPSK and 8 PSK, 16 QAM and 64 QAM, DSB-AM and SSB-AM signals, which are extremely similar in terms of time-frequency characteristics, noise and interference make it difficult to distinguish between otherwise subtle phase or amplitude changes [24]. As a result, under low SNR conditions, these three types of modulated signals are easily confused with each other during the identification process, leading to a reduction in the identification accuracy, as shown in Figure 8. The confusion matrices of the CDRNN at 18 dB and 12 dB are given in Figure 9, which shows that even under high SNR conditions, QPSK and 8 PSK, 16 QAM and 64 QAM, DSB-AM and SSB-AM are partially confused. Moreover, the reduction in SNR exacerbates the confusion of these three types of modulations. It is calculated that the average recognition accuracy of the CDRNN in Figure 9 is 78% for these confusing modulations, which is higher than the 66% of MobileNet and the 65.08% of AWN. Figure 8: Accuracy of modulation types in fading channel over various SNRs. Under the channel model in Section 3.2, simulation experiments were conducted for comparison. 16 QAM and 64 QAM cause some confusion, while DSB-AM and SSB-AM show significant overlap. Figure 9: _Cont._  
Figure 9: Confusion matrix diagram. (**a**) CDRNN. (**b**) MobileNet. (**c**) AlexNet. (**d**) AWN. The values represent the proportion of predictions for each class, normalized by the true labels.