## 4 Experimental Results  
We used the CWT and deep convolutional neural network (CNN) to analyze the digital modulation categorization method. The modulation techniques used to encrypt decimal data in the range of 1 to 255 were many. MATLAB was used to create a total of six distinct SNR rates, each with a 5 dB increase, in a range from 0 dB to 25 dB. Two different experimental conditions were created for the digital modulation categorization task. In the first experiment, a variety of SNR rates were used to evaluate the classification accuracy of the proposed model. This experiment was run five times, raising the SNR rate from 0 dB to 25 dB in 5-decibel increments, for each class of 255 samples. The information from each grade level was then compared with the others. The second experiment produced 1530 samples, with SNRs ranging from 0 to 25 dB for each class. Thus, 9180 spectrogram pictures were used to feed the suggested model. We also compared the outcomes of every experiment with one another. Several preprocessing techniques were applied to the scalogram pictures. To eliminate any undesirable white areas, the acquired standard CWT pictures were first automatically cropped. Next, the resolutions were decreased from 657 \(\times\) 535 \(\times\) 3 to 227 \(\times\) 227 \(\times\) 3 pixels. This made it possible to focus on certain areas of interest in the scalogram pictures. All training was carried out using MATLAB (2021a) and an NVIDIA graphics card (8 GB onboard RAM). We examined the model's classification ability at different SNR rates between 0 and 25 decibels (dB) in terms of our prior results. As a result, for each class, only 255 scalogram images with a single SNR rate were taken into account. To put it another way, a total of 1530 spectrogram pictures were used to feed input to the CNN model. Experiments on the modulation classification issue were carried out by randomly dividing the dataset into training (70%) and testing (30%) groups. The suggested model contains a diagonal pattern in the confusion matrix for modulation-type recognition. It highlights the model under consideration's high level of categorization accuracy. The results of the experiment are reported in Table 1, as a function of the SNR rates. The total accuracy of all SNRs comes out to be over 99.9, indicating that the suggested approach is noise resistant. Equations (7)-(10) are used to compute accuracy, precision, recall, and \(F1\)-score measurements in order to assess the model's effectiveness in the sequence of 7, 8, 9, and 10. These measurements are computed using the true positive (TP), false negative (FN), false positive (FP), and true negative (TN) indices. TP and TN reflect the number of positive and negative samples that were properly identified, while FP and FN reflect the number of positive and negative samples that were incorrectly classified. \[Accuracy=\left(\frac{total\ number\ of\ correct\ samples}{number\ of\ total\ samples}\right)_{for\ i^{th}class} \tag{7}\]  
\[Precision=\left(\frac{true\ positives}{true\ positives+false\ positives}\right)_{for\ i^{th}class} \tag{8}\] \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**SNR**} & \multicolumn{5}{c}{} \\ \cline{2-9}  & **0 dB** & **5 dB** & **10 dB** & **15 dB** & **20 dB** & **25 dB** & **Avg. Acc** \\ \hline \(ASK\) (Proposed) & 99.84 & 99.88 & 99.90 & 99.91 & 99.93 & 99.97 & 99.90 \\ \(FSK\) (Proposed) & 99.58 & 99.60 & 99.70 & 99.91 & 99.94 & 99.94 & 99.77 \\ \(PSK\) (Proposed) & 99.99 & 99.99 & 99.99 & 100 & 100 & 100 & 99.99 \\ QASK (Proposed) & 99.99 & 99.99 & 99.99 & 100 & 100 & 100 & 99.99 \\ QPSK (Proposed) & 99.99 & 99.99 & 99.99 & 100 & 100 & 100 & 99.99 \\ QPSK (Proposed) & 99.99 & 99.99 & 99.99 & 100 & 100 & 100 & 99.99 \\ Avg. ACC (Proposed) & 99.90 & 99.91 & 99.92 & 99.97 & 99.978 & 99.98 & 99.938 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The proposed model’s accuracy when different SNR rates are considered independently.