# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 5 Experimental Results and Discussion
### Efficiency of the Inception Blocks  
We constructed the DIN classifier based on the inception blocks. To confirm the efficiency of the inception blocks, the identification accuracy of the proposed method was compared with that of baseline 3. The difference between the proposed method and baseline 3 lies in the classifier. As in baseline 3, the classifier was set to the residual-based classifier described in [8]. Two experiments were performed for comparison. One was conducted to identify the emitter ID from the received hop signal \(\mathbf{s}\) without the SF extraction, and the  
Figure 10: Emitter identification accuracy at different signal-to-noise ratios (SNRs).  
other was performed to identify the emitter ID from the ensemble approach of the SFs. The results are presented in Table 5 and Figure 11.  
Table 5 presents the identification accuracies of the proposed algorithm and baseline 3. The identification accuracy results at different SNRs are presented in Figure 11. Both sets of results demonstrate the efficiency of the inception blocks. Table 5 reveals that the DIN-based approach can produce higher accuracies than the residual-based approach. This result is also shown in Figure 11. As the SNR changes, the accuracy of the DIN-based approach is superior to that of the residual block-based approach, except when the ensemble approach of the residual-based method overcomes the hop and DIN-based method in environments with SNRs of 20 dB or more. However, if we focused on the classifier structure, i.e., compared the performance between hops approaches or ensemble approaches, the performance of the residual network could not overcome the performance  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Predicted Emitter (\%)**} \\ \cline{3-10}  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline \multirow{6}{*}{Actual Emitter (\%)} & 1 & 100.0 & 0 & 0 & 0 & 0 & 0 & 0 \\  & 2 & 0.2 & 98.6 & 0 & 0.2 & 0.4 & 0 & 0.6 \\ \cline{1-1}  & 3 & 0 & 0 & 98.0 & 0.2 & 0 & 1.8 & 0 \\ \cline{1-1}  & 4 & 0 & 1.6 & 0.6 & 95.5 & 0.6 & 0.4 & 1.4 \\ \cline{1-1}  & 5 & 0 & 0.2 & 1.9 & 0.4 & 96.0 & 1.0 & 0.4 \\ \cline{1-1}  & 6 & 0 & 0 & 2.6 & 0 & 1.0 & 95.8 & 0.6 \\ \cline{1-1}  & 7 & 0.6 & 1.0 & 0.4 & 2.8 & 0.6 & 0 & 94.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Averaged confusion matrix of the ensemble approach based proposed method.  
Figure 11: Identification accuracies of the residual and inception blocks at different SNRs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Hop Signal** & **Ensemble Approach** \\  & **without SF Extraction** & **with SF Extraction** \\ \cline{2-5}  & **Mean Accuracy (\%) \(\pm\) Standard Deviation** \\ \hline \multirow{2}{*}{Spectrogram—Residual \({}^{***}\)} & 94.4 \(\pm\) 1.1 & 96.4 \(\pm\) 0.7 \\ \cline{1-1}  & 95.1 \(\pm\) 1.0 & 97.0 \(\pm\) 0.6 \\ \hline \hline \end{tabular} \({}^{***}\): (Baseline 3) spectrogram approaches in [8]. \({}^{*}\): (Proposed) spectrogram approach of SF.  
\end{table}
Table 5: Identification accuracies of the residual and inception blocks.  
of the inception blocks. As described in Section 3.3.1, this result may stem from the fact that filtering features with different receptive field sizes can help train SFs within deep learning architectures.