# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 5 Experiment and Analysis
### Comparison of AMC Performance  
For non-cooperative communication applications, the receiver needs to firstly perform modulation classification, followed by demodulation and decoding. Therefore, the modulation classification experiment is also done to further verify the performance of the denoising autoencoder. The LMS and RLS algorithms need to know the modulation mode and send the training sequence when they are used to reduce noise in communication systems, leading to low transmission efficiency. Thus, they are unsuitable for non-cooperative communication applications. PCA also has the lowest denoising performance. As a result, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **RLS** & **LMS** & **PCA** & **DnCNN** & **DRdA-CA** \\ \hline GMSK & 6.76 dB & 6.50 dB & 7.18 dB & 11.59 dB & 16.84 dB \\ CPPSK & 6.14 dB & 5.75 dB & 8.45 dB & 10.11 dB & 15.39 dB \\ BPSK & 6.15 dB & 5.74 dB & 8.46 dB & 11.12 dB & 11.86 dB \\ QPSK & 6.14 dB & 5.74 dB & 6.21 dB & 9.17 dB & 13.44 dB \\ QQPSK & 6.14 dB & 5.76 dB & 6.17 dB & 8.99 dB & 13.43 dB \\
8 PSK & 6.13 dB & 5.74 dB & 5.44 dB & 8.29 dB & 9.47 dB \\
16 QAM & 6.13 dB & 5.75 dB & 5.43 dB & 7.9 dB & 8.38 dB \\
64 QAM & 6.14 dB & 5.78 dB & 6.19 dB & 7.79 dB & 8.27 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average value of SNR improvement under different denoising methods. Figure 8: EVM after denoising of modulation signal [2; 3; 5; 6; 7; 18]. (a) GMSK. (b) 8 PSK. (c) 16 QAM. (d) 64 QAM. in the experiment of the noise reduction cascade AMC, we compare the DrCNN to the proposed method. Moreover, the CNN [34] is adopted to implement AMC, and the dataset is given in Section 4.1, including eight modulation signals under eleven SNRs. The experiment schemes are as follows. (1) The CNN model is used for AMC directly, denoted as CNN. (2) The DrCNN method is used to reduce the noise of modulation signals, and then CNN is used for AMC, denoted as DrCNN+CNN. (3) The proposed DRdA-CA is used to decrease the noise, followed by CNN for AMC, denoted as DRdA-CA+CNN. Figure 9 displays the average classification accuracy of all the modulation modes for three different schemes. The modulation recognition accuracy is greatly increased when using denoising methods based on deep learning. The DRdA-CA method clearly outperforms the DrCNN method. When the SNR varies from \(-12\) dB to \(8\) dB, compared with the non-denoising scheme, the scheme with DnCNN denoising improves the average classification accuracy by 0.01%\(\sim\)12.5%. After using the proposed denoising approach, the average classification accuracy is increased by 1.88%\(\sim\)29.25%. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase from 67.59%\(\sim\)74.94% over the entire SNR range, which further shows the superiority of the proposed method. Figure 9(a),b give the recognition accuracy of each modulation type with the latter two schemes. As seen in Figure 9(a), after DnCNN denoising, the recognition accuracy of the three low-order modulations, BPSK, GMSK, and CPFSK, is relatively high, and almost reaches 100% under the SNR of \(-4\) dB. This is because these three types are low-order modulations and the patterns of the signals are simple and significantly different from those of the high-order PSK and QAM. Therefore, their classification accuracy is also high, even through general DnCNN denoising. However, the recognition performance of the high-order modulations is poor. In particular, the recognition accuracy of the 16 QAM and 64 QAM signals fluctuates widely. This is attributed to the highly similar characteristics of higher-order modulation signals and the insufficient noise reduction capability of DnCNN. It should be noted that the classification accuracy of QPSK and OQPSK is lower than that of 8 PSK in most of the SNR intervals, although their modulation order is smaller than that of 8 PSK.