## 4 Results
### Ablation Study

Table 6: The average improvement brought to SigAugment-R by each transformation. In terms of gain, we highlighted the top two transformations. In a real communication environment, the low-data category of modulation is not constant. We construct datasets with different majority and minority classes in two ways. (1) By reversing the default list of modulation classes, i.e., changing the order from 8 PSK, \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**2016A-1**} & \multicolumn{2}{c}{**2016A-10**} & \multicolumn{2}{c}{**2016A-10a**} & \multicolumn{2}{c}{**2016A-10b**} & \multicolumn{2}{c}{**2016A-20**} & \multicolumn{2}{c}{**2016A-50**} \\ \cline{2-11}  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline Baseline & 79.83 & 0.53 & 71.08 & 0.82 & 66.35 & 0.46 & 58.37 & 0.38 & 63.64 & 2.22 & 54.05 & 0.72 \\ Joint Augmentation & 83.12 & 0.11 & 79.46 & 0.79 & 76.06 & 0.86 & 70.31 & 0.48 & 77.23 & 0.81 & 68.42 & 1.16 \\ SigAugment-R & 83.63 & 0.46 & 81.36 & 0.07 & 78.49 & 0.31 & 73.26 & 0.13 & 78.32 & 0.99 & 70.93 & 1.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the proposed method with the joint augmentation method. Figure 11: Validation on the 2016A-10b dataset of (**a**) the effect of the maximum number of transformations on SigAugment-R and (**b**) the effect of the number of transformations on SigAugment-C. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**2016A-1**} & \multicolumn{2}{c}{**2016A-10**} & \multicolumn{2}{c}{**2016A-10a**} & \multicolumn{2}{c}{**2016A-10b**} & \multicolumn{2}{c}{**2016A-20**} & \multicolumn{2}{c}{**2016A-50**} \\ \cline{2-11}  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline Baseline & 79.83 & 0.53 & 71.08 & 0.82 & 66.35 & 0.46 & 58.37 & 0.38 & 63.64 & 2.22 & 54.05 & 0.72 \\ Joint Augmentation & 83.12 & 0.11 & 79.46 & 0.79 & 76.06 & 0.86 & 70.31 & 0.48 & 77.23 & 0.81 & 68.42 & 1.16 \\ SigAugment-R & 83.63 & 0.46 & 81.36 & 0.07 & 78.49 & 0.31 & 73.26 & 0.13 & 78.32 & 0.99 & 70.93 & 1.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the proposed method with the joint augmentation method. AM-DSB, BPSK, CPFSK, GFSK, PAM4, QAM16, QAM64, QPSK, and WBFM to WBFM, QPSK, QAM64, QAM16, PAM4, GFSK, CPFSK, BPSK, 8 PSK, and AM-DSB. (2) By randomly shuffling the list but fixing the random seeds during each training session so that all experiments are repeatable, the order of the modulation classes obtained is AM-DSB, CPFSK, WBFM, BPSK, 8 PSK, QAM16, PAM4, GFSK, QPSK, and QAM64. This gives us six new unbalanced datasets. Detailed information can be found in Tables 15 and 16 in Appendix A.3. Based on previous experiments, the experimental protocol for these six datasets is to select one representative method from each of the rebalancing and data augmentation methods to experiment with. These two methods are Polynom_fit_SMOTE_poly and SigAugment-R. The experimental results are shown in Tables 17 and 18. We obtained similar results to the previous experiments. However, we also note that for the DAE model, our proposed SigAugment method is not always valid. This may be related to the encoder structure of a network such as DAE. Overly complex data augmentation is difficult to encode and decode effectively for the DAE model.