# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
## 5 Experiments
### Comparisons  
Tables 1-3 contain the main results on the _mobile_ and _radio_ datasets, respectively. It was found that the proposed \(\text{OSSG}_{TW}\) significantly improved the results of all the indicators on both the _mobile_ and _radio_ datasets. Compared with the classical APRL algorithm, the accuracy indicator on the known classes was improved by 5.18%, while the AUC indicator was improved by 3.26%. As for the unknown categories, the accuracy index was increased by 3.13%. The most significant improvements included the increase of up to 4.26% in the PRE index and up to 5.37% in the AUC index with respect to the known and unknown datasets, respectively. The proposed \(\text{OSSG}_{TW}\) algorithm was compared with some ablation versions, which also shows the efficiency of the proposed three basic sub-modules. It was found that the effect of using the ViT model to extract the features and employing the center loss to reduce the distance within the target feature classes produced a 1-2% improvement in each index, while the Wasserstein distance produced less than a 1% improvement due to the overall effect.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**PRE (\%)**} & \multicolumn{2}{c}{**AUC (\%)**} \\ \cline{2-7}  & **Known** & **Unknown** & **Known** & **Unknown** & **Known** & **Unknown** \\ \hline \multirow{2}{*}{OpenMax} & 82.62 & 86.31 & 81.70 & 85.07 & 89.57 & 89.79 \\  & (\(\pm\)2.11) & (\(\pm\)1.38) & (\(\pm\)2.91) & (\(\pm\)2.53) & (\(\pm\)1.97) & (\(\pm\)3.10) \\ \multirow{2}{*}{G-OpenMax} & 84.84 & 87.98 & 83.29 & 84.74 & 90.90 & 90.84 \\  & (\(\pm\)3.12) & (\(\pm\)2.53) & (\(\pm\)2.56) & (\(\pm\)1.01) & (\(\pm\)2.47) & (\(\pm\)2.01) \\ \multirow{2}{*}{CROSR} & 87.29 & 90.62 & 88.01 & 89.62 & 92.37 & 92.18 \\  & (\(\pm\)2.91) & (\(\pm\)1.76) & (\(\pm\)2.32) & (\(\pm\)2.77) & (\(\pm\)2.21) & (\(\pm\)1.34) \\ \multirow{2}{*}{ARPL} & 88.64 & 91.70 & 89.38 & 91.21 & 93.18 & 91.41 \\  & (\(\pm\)2.83) & (\(\pm\)2.14) & (\(\pm\)2.23) & (\(\pm\)1.65) & (\(\pm\)1.92) & (\(\pm\)1.71) \\ \multirow{2}{*}{SARPL} & 88.94 & 92.18 & 89.55 & 91.98 & 93.36 & 92.98 \\  & (\(\pm\)2.16) & (\(\pm\)2.05) & (\(\pm\)1.93) & (\(\pm\)1.44) & (\(\pm\)2.19) & (\(\pm\)1.75) \\ \hline \multirow{2}{*}{\begin{tabular}{c} \(\text{OSSG}_{TW}\) \\ (w/o \\ W/CL) \\ \end{tabular} } & 91.58 & 93.01 & 92.93 & 92.29 & 94.75 & 93.12 \\  & (\(\pm\)1.34) & (\(\pm\)1.28) & (\(\pm\)1.19) & (\(\pm\)1.12) & (\(\pm\)1.23) & (\(\pm\)1.38) \\ \multirow{2}{*}{\
\begin{tabular}{c} \(\text{OSSG}_{TW}\) \\ (w/o CL) \\ \end{tabular} } & 92.01 & 93.29 & 92.45 & 92.77 & 95.21 & 93.53 \\  & (\(\pm\)2.24) & (\(\pm\)1.23) & (\(\pm\)1.02) & (\(\pm\)1.43) & (\(\pm\)1.38) & (\(\pm\)1.48) \\ \multirow{2}{*}{
\begin{tabular}{c} \(\text{OSSG}_{TW}\) \\ (w/o CL) \\ \end{tabular} } & **93.82** & **94.83** & **94.21** & **94.62** & **96.44** & **95.32** \\  & (\(\pm\)1.94) & (\(\pm\)1.73) & (\(\pm\)1.12) & (\(\pm\)2.26) & (\(\pm\)1.65) & (\(\pm\)1.76) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of the phones.