# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
## 5 Experiment and Analysis
### Comparison of SNR Improvement  
In the second experiment, we compare the SNR and EVM variations of different denoising methods. Table 4 displays the average values of SNR improvement in overall SNR, ranging from \(-12\) dB to 8 dB for all eight modulation modes with five different denoising methods. In the SNR range of \(-12\) dB to 8 dB, taking the GMSK signal as an example, the SNRs of the modulation signal are increased by 6.76 dB, 6.5 dB, and 7.18 dB, respectively, on average, after denoising with RLS, LMS, and PCA algorithms. When the DnCNN model is used for noise reduction, the SNRs of the signal are improved by 11.59 dB on average on average. Remarkably, the proposed DRdA-CA brings an increase of 16.8 dB on average in terms of the SNRs. When DRdA-CA is applied to 8 PSK, 16 QAM, and 64 QAM signals, the average values of SNR enhancement are 9.47 dB, 8.38 dB, and 8.27 dB, respectively. For two deep learning-based denoising methods, DnCNN and DRdA-CA, the average values of SNR improvement gradually go up with the decrease in modulation order, except in a few cases. However, traditional noise reduction methods do not show similar laws. It is clear that DRdA-CA outperforms the DnCNN method and RLS, LMS, and PCA. This can be attributed to the well-designed structure in the proposed method, in which both double-residual connections and the introduction and improvement of the SE module contribute to a desirable denoising effect.  
It would be complicated to use curves to display dynamic ranges of SNR and EVM after denoising with the increase of SNR for all eight modulation modes. As a result, we only present four typical modulation modes in Figures 7 and 8. Figure 7a-d respectively show the SNR variations of GMSK, 8 PSK, 16 QAM, and 64 QAM, and four signals are processed using different denoising methods. It can be seen from Figure 7 that the deep learning  
Figure 6: Ablation experiments. (**a**) Original SE and improved SE. (**b**) Single and dual residual connections.  
denoising methods, denoted as DRdA-CA and DnCNN, are superior to the three traditional denoising methods, denoted as LMS, RLS, and PCA. The proposed denoising method achieves different denoising effects for different modulation modes. Although denoising is more challenging for high-order modulation due to the complex signal patterns, the proposed denoising achieves a desirable denoising performance for high-order modulation. Moreover, for the four modulation types, the proposed DRdA-CA has always maintained the maximum SNR improvement over the entire SNR range.  
Figure 8a-d also show the EVM curves of the above four modulation signals processed using different noise reduction methods. The lower the EVM, the better the noise reduction performance. As seen from Figure 8, the EVMs obtained using the two deep learning-based denoising methods are significantly lower than those obtained using the other three traditional denoising methods. After PCA denoising, the EVMs of the signals do not steadily decline with the increase in the SNR, which indicates that the robustness of PCA is poor. The signal EVM curves obtained after RLS and LMS denoising are close to each other when the SNR is greater than 4 dB. However, when the SNR falls in \(-12\) dB\(\sim\)4 dB, RLS is better than LMS. The deep learning algorithm DnCNN performs better than the three traditional algorithms do, and the proposed DRdA-CA makes a better achievement in denoising than DnCNN does.  
Figure 7: SNR of modulation signal after denoising [2; 3; 5; 6; 7; 18]. **(a)** GMSK. **(b)** 8 PSK. **(c)** 16 QAM. **(d)** 64 QAM.