## 5 Experiments
### Model Recognition Accuracy Evaluation  
In contrast to DL-based models relying on IQ inputs, this paper employed TFI inputs. Consequently, the parameter setting of STFT time-frequency conversion exerted an impact on the accuracy of modulation recognition. Among various factors, the window length stands as a crucial element that influences both the complexity of the STFT transformation and the transformation effect. Here, we focused on examining the influence of different window length STFT transformations on modulation recognition. We set the window length to \(N=20\), \(35\), \(50\) and \(80\) and compared their respective recognition accuracies. As shown in Figure 5, the recognition accuracy at any signal-to-noise ratio for a window length of \(N=20\) surpasses that of the other three cases where \(N=35\), \(N=50\) and \(N=80\). At low SNRs, recognition accuracies are similar regardless of window length. However, at high SNRs, shorter window lengths lead to higher recognition accuracy. This indicates that shorter window lengths are better at depicting the time-frequency characteristics of modulated signals. Nevertheless, they also give rise to a greater computational burden. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Parameter** & **CDRNN** & **AlexNet** & **ResNet50** & **MobileNet** & **TFA-SCNN** &
\begin{tabular}{c} **STFT-** \\ **CDSC** \\ \end{tabular} & **VViT** \\ \hline Model Size & 4.4 MB & 217.6 MB & 91.8 MB & 6.5 MB & 4.6 MB & 23.23 MB & 46.26 MB \\ Number of parameters & \(1.15\times 10^{6}\) & \(5.7\times 10^{7}\) & \(2.75\times 10^{7}\) & \(1.71\times 10^{6}\) & \(1.21\times 10^{6}\) & \(6.09\times 10^{6}\) & \(1.39\times 10^{7}\) \\ Training times & 2.12 ms & 13.7 ms & 5.01 ms & 1.71 ms & 2.03 ms & 2.75 ms & 3.61 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key numerical indicators of deep neural networks. Figure 4: Results of ablation experiments. The CDRNN was divided into two categories: the model that does not use the CA mechanism and the model that does not use CRJs. Ablation experiments were conducted to verify the effectiveness of these models. Figures 6 and 7 show the recognition accuracy of the deep learning-based schemes at different SNRs in dB scale. The former constitutes a simulation under Gaussian channel conditions, in which the channel model parameters of Section 3.2 are adjusted so as to retain solely Gaussian white noise. The latter represents a simulation experiment conducted under the channel model conditions of Section 3.2. As Figure 6 demonstrated, in the ideal Gaussian channel environment, the image-based CDRNN achieves the highest level of recognition accuracy under any circumstances of SNR. The deep learning methods based on IQ signals exhibit extremely excellent accuracy, being only slightly less accurate than the CDRNN model. The accuracy of other deep learning models with image input is marginally lower when compared with that of the CDRNN, AWN and CNN-IQ. At SNR greater than or equal to 0 dB, the recognition accuracies of all methods are above 80%. Nevertheless, as the SNR decreases, the performance of each deep learning method gradually deteriorates. When the SNR is lower than 0 dB, it will exert a significant impact on the recognition accuracy. It can be clearly seen that the SNR is a crucial element that influences the modulation recognition accuracy. Figure 5: Accuracy of AMR schemes over various window lengths \(N\). Under the channel model in Section 3.2, simulation experiments were carried out to make a comparison of the recognition accuracy of STFT’s window length. Figure 6: Accuracy of AMR schemes in Gaussian channel over various SNRs. Adjusting the channel model in Section 3.2 to retain only Gaussian white noise, simulation experiments were carried out to make a comparison of the recognition accuracy of different DNNs. As shown in Figure 7, the performance of all DNNs decreases significantly under a fading channel that encompasses AWGN, multipath fading, CFO, and SRO.