## 4 Results and Discussion
### Comparative Experiments of Various Networks  
The two datasets mentioned in Table 1 are used to train the neural networks and evaluate their performance. The \"Glorot\" uniform initializer is utilized to initialize the weights of the convolutional layers in each network, while the \"He\" initializer is employed to initialize the weights of the fully connected layers. The training weights are learned through the Adam optimizer by keeping a learning rate of 0.0001, which minimizes the categorical cross-entropy loss function. We compared the performance of the proposed model with the existing modulation classification methods, namely GRU2, CLDNN [50], ResNet, Inception [51], 2 DCNN [35], and ANN [52]. The proposed design presents a lower number of parameters with low computational complexity, as shown in Table 2. The total number of parameters of the proposed model was 253,274, which is fewer than the contemporary automatic modulation classification that focused on parameter reduction using alternative methods for performing convolution. The proposed model outperforms the baseline inception [53] method by having parameter reductions of 98.9%, which is a higher amount of reduction than the other contemporary methods. For datasets RML 2016.10a and RML 2016.04c, the number of parameters and amount of reduction in terms of percentage with the contemporary approach are tabulated in Table 2. It can be seen that compared to the GRU2 method, the proposed method shows a 79.9% parameter reduction, whereas compared to ResNet, the proposed model shows a 88.7% reduction in parameters. Similarly, a reduction of 89.1% and 96.1% in the number of parameters of the proposed method was observed compared to CLDNN [50], Conv2D [12], and 96.22% 2 DCNN [35], respectively. When compared to ANN [52], depthwise convolution, and depthwise separable convolution [12], parameter reductions of 97.2%, 97.5%, and 98.3% were observed for the proposed method. We also compared the number of parameters of the proposed method with that of the pruned network. In particular, AlexNet was pruned and utilized for the classification of radio modulations [54]. The accuracy graph is also plotted in Figures 6 and 7 for \(D1\) and \(D2\), respectively. \begin{table}
\begin{tabular}{l l} \hline \hline
**Classes** & BPSK, AM-DSB, AM-SSB, BPSK, QPSK, BFSK, CPFSK, \\  & QAM16, QAM64, PAM4, WB-FM \\ \hline
**Sample length** & 128 \\ \hline
**SNR Range** & \(-20\) dB to \(18\) dB \\ \hline
**Databases** & RadioML 2016.04C, RadioML 2016.10A \\ \hline
**Number of Samples** & 162,060, 220,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Database representation. We also evaluated the effectiveness of the proposed AMC method by comparing it with popular traditional feature-based methods, including Gaussian naive Bayes, Bernoulli \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Architecture** & **Parameters** & **Reduction** & **FLOPs** \\ \hline Inception [51] & 23.9 M & – & 1502 M \\ \hline GRU2 [50] & 4.8 M & 79.9\% & 0.234 M \\ \hline ResNet [53] & 2.7 M & 88.7\% & 149 M \\ \hline CLDNN [50] & 2.6 M & 89.1\% & 1.65 M \\ \hline Conv2D [12] & 921,611 & 96.1\% & 3.15 M \\ \hline
2 DCNN [35] & 900,000 & 96.2\% & 29.6 K \\ \hline ANN [52] & 670,000 & 97.2\% & NA \\ \hline Depthwise [12] & 596,491 & 97.5\% & 3.58 M \\ \hline Separable [12] & 385,307 & 98.3\% & 18.6 M \\ \hline Pruned CNN [54] & 36.6 M & NA & 116.6 M \\ \hline Proposed & 253,274 & 98.9\% & 748.88 M \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of proposed method with contemporary approaches in terms of parameter reduction and number of parameters on 18 dB SNR. Figure 6: Classification accuracy of the various network architectures on \(D1\). Figure 7: Classification accuracy of the various network architectures on \(D2\). naive Bayes, RBF-SVM, linear-SVM, decision tree, random forest, KNN, and ELM [35; 55; 56]. Figure 8 displays the classification accuracy at various SNRs.