# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
## 5 Discussion and Conclusions  
In this paper, an end-to-end deep learning-based method for carrier signal detection in the broadband power spectrum, so-called SCN, was proposed. By regarding the carrier signal problem as an object localization task in the 1D broadband power spectrum image rather than the segmentation task in the FCN-based method, the core task is to regress the centers of all subcarriers and their corresponding bandwidths. To improve the FCN-based method, we apply the attention mechanism and use more complex residual layers in the feature extraction modules. Then, the FPN neck fused the different scales of features and automated regressions to PSD and BW predictions by the RegNet head. As shown in Table 2, the experiments have suggested that the SCN method, through training with the simulation samples, effectively handles the carrier signal detection problem and achieves higher performance than the FCN- and threshold-based methods. Moreover, in the training process, Figures 6 and 7 suggest that the proposed model converges with the training epochs. Meanwhile, increasing the scale of the model or using more numbers of training samples both improve the PSD and BW loss to a lower value. However, while the SCN scale is beyond 8, the distance of performance on the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Time Cost/ms** & **FLOPs/M** & **Parameters/K** \\ \hline SigdetNet with FocalLoss & 15.32 & 909.97 & 297.52 \\ \hline FCN-Based 1 & 2.01 & 6.29 & 16.03 \\ \hline FCN-Based 2 & 3.32 & 7.86 & 25.44 \\ \hline FCN-Based 3 & 3.49 & 8.65 & 34.85 \\ \hline FCN-Based 4 & 4.78 & 9.04 & 44.26 \\ \hline FCN-Based 5 & 5.24 & 9.24 & 53.66 \\ \hline FCN-Based 6 & 5.71 & 9.34 & 63.07 \\ \hline FCN-Based 7 & 6.14 & 9.39 & 72.48 \\ \hline FCN-Based 8 & 6.46 & 9.41 & 81.89 \\ \hline FCN-Based 9 & 7.23 & 9.42 & 91.3 \\ \hline FCN-Based 10 & 7.93 & 9.43 & 100.7 \\ \hline FCN-Based 11 & 9.11 & 9.43 & 110.11 \\ \hline FCN-Based 12 & 9.71 & 9.44 & 119.52 \\ \hline FCN-Based 13 & 12.28 & 9.44 & 128.93 \\ \hline SCN-6\(\times\) & 8.98 & 12,923.99 & 3312.23 \\ \hline SCN-7\(\times\) & 10.25 & 13,338.22 & 4120.42 \\ \hline SCN-8\(\times\) & 11.45 & 13,545.35 & 4928.61 \\ \hline SCN-9\(\times\) & 12.85 & 13,648.92 & 5736.8 \\ \hline SCN-10\(\times\) & 14.36 & 13,700.71 & 6544.99 \\ \hline
**SCN-11\(\times\)** & 16.7 & 13,726.62 & 7353.19 \\ \hline SCN-12\(\times\) & 16.83 & 13,739.58 & 8161.38 \\ \hline
**SCN-13\(\times\)** & 18.69 & 13,746.07 & 8969.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The complexity comparison between the proposed method and the other deep learning-based methods. validation simulation samples becomes smaller as the training epochs increase, as shown in Figure 8. Furthermore, in Figure 9, the number of training samples slightly impacts the validation performance when the losses converge. Considering that the validation samples are simulated in the same conditions as the training samples, our analysis indicates that the model tends to overfit as the losses converge. Increasing the variety of sample generation conditions or the actual signal sample numbers would help alleviate this phenomenon. Finally, in the evaluation phase, the performance increases with the SCN scale, as shown in Table 2. Meanwhile, Table 3 indicates that the SCN method requires significant computation complexity, which causes more inference time than the FCN-based methods, even though the SCN methods do not require post-processing. Therefore, in our future work, we plan to reduce the computation complexity of the proposed method to allow for more efficiency in real applications. Conceptualization, H.H. and J.L. ; methodology, H.H. ; software, H.H., P.W. and J.W. ; validation, P.W. and J.W. ; writing--original draft preparation, H.H. ; writing--review and editing, J.L. All authors have read and agreed to the published version of the manuscript. This work was supported by Science and Technology on Electronic Information Control Laboratory (Grant No. 6142105190310).