## 4 Results and Discussion
### Comparison with Contemporary Methods  
In [7], an AMC technique using \(D2\) was presented, in which the use of a convolution neural network was investigated in the domain of complex-valued temporal radio signals. The efficacy of radio modulation classification using naively learned features was compared to expert feature-based techniques, and the results show significant performance. However, their proposed approach yielded 91.91% at 18 dB SNR. In [12], the authors proposed deep learning techniques to meet the latency requirements in Internet of Things (IoT) applications. An effective convolution neural network based on 2D convolution, depthwise convolution, and depthwise separable convolution was presented to classify the modulation of received signals. On \(D1\) and \(D2\), their models achieved 71.3% and 87.4% accuracy with Conv2d convolution at 18 dB SNR, 69.90% and 92.10% with depthwise convolution, and 71.25% and 83.03% with depthwise separable convolution. Modified CNN was utilized in [35] to classify dataset \(D2\). On the basis of the naively learned technique, the results were retrieved to determine the best features for radio modulation classification. Their approach obtained 87.4% accuracy at 18 dB SNR. In [36], the authors deployed a signal distortion correction module (CM) to enhance the performance of modulation recognition schemes. They demonstrated that their suggested module with a CNN approach can achieve better accuracy than the CNN and CLDNN. Furthermore, their model was based on a neural network that can be assumed as estimator of carrier frequency and phase offset presented by the channel. They achieved 80% accuracy at an SNR above \(-14\%\) dB, with significant improvement above 0 dB on dataset \(D1\). In [57], IQ-modulated radio frequency signals were classified analytically by using reservoir computing on narrowband optoelectronic oscillators (OEOs) operated by a continuous-wave semiconductor laser. Additionally, they designed high-Q OEOs for ultralow-phase noise microwave production consisting of efficient hardware architecture to process such multi-GHz modulated signals. However, their architecture achieved 89% accuracy at 18 dB SNR on dataset \(D2\). However, compared to the prior work, our proposed model can learn latent information from received signals repeatedly, resulting in a higher accuracy of 81.0% and 94.05% for \(D1\) and \(D2\), respectively, at 18 dB SNR, with fewer parameters. Results of the aforementioned methods are tabulated in Table 5.  
Furthermore, Figure 10 demonstrates the confusion matrices for both datasets at an SNR value of 18 dB. In a confusion matrix, the real component is represented by each row, while the prediction component is represented by each column. Figure 10b illustrates the  
\begin{table}
\begin{tabular}{|p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}|} \hline \hline
**Paper** & **Dataset** & **Accuracy Per SNR** & **Classification Method** & **Architecture** \\ \hline \([7]\) & Radio ML2016.04C & 91.91\% for SNR \(-18\) dB, Range \([-20,+18]\) & Feature-Based & VICNN2 \\ \hline \([12]\) & \(D1=\) BMI2016.10A, \(D2=\) BMI2016.04C & 69.90\% for SNR \(-18\) dB for \(D1\), and 92.10\% at SNR \(-18\)  
performance of the proposed network for dataset RML2016.04.C. The confusion matrix shows the superior classification performance of the network surpassing the contemporary methods. Figure 10a shows the classification performance on \(D1\), which indicates the two main factors affecting the classification accuracy at high SNR. WBFM is misclassified as AM-DSB; this situation could be the result of the analog audio stream in the dataset, which is a silent period. On the other hand, the constellation points of QAM16 are similar to the constellation points in QAM64; the network misclassified the modulations as QAM64, and may exhibit similar patterns of noise and inference. Furthermore, it has been shown in a study that some of the SNR values in 64-QAM have two different noise floor values, making the classification problem difficult [59].