## 8 Conclusions and Future Developments  
This paper presented a study on the application of time-frequency transforms in combination with CNN for the problem of channel identification. The recognized performance (in the research literature) of CNN for the classification of images has been exploited by converting digitized radio frequency signals to images using TFTs. The results show that this approach is preferable in terms of identification accuracy than the application of CNN directly to the 1-dimensional representation of the signals as commonly done in literature at the cost of performing the specific TFT. An extensive evaluation of the discriminating power of the different TFTs has been performed in this paper for a data set based on pulse-like weather radar signals in two  
Figure 14: Confusion matrices for the weather radar data set with DIFFNSR method. (**a**) Confusion matrix for CWT-G and SNR = 0 dB; (**b**) Confusion matrix for the 1D original time representation and SNR = 0 dB. different configurations. One configuration where the training and test portions of the data set have the same values of SNR in dB and another where the training and test portions of the data set have different values of SNR in dB. The results show that an accurate selection of the TFT is important to obtain an optimal classification in both configurations. In addition, there is a potential trade-off between high channel identification accuracy and robustness to noise. The application of CWT usually provides superior performance, especially in the presence of noise to the other time-frequency transform, but it presents the challenge of choosing the optimal mother wavelet. An exhaustive approach would require the repetition of the CNN classification for each mother wavelet, which will be demanding from the computing point of view. Then, this paper presented an adaptive algorithm to select the optimal mother wavelet based on the application of feature extraction algorithms and'shallow' machine learning, which has a minor impact on the overall computing time (less than 2% overhead in comparison to the CNN classification) but it supports the optimal or quasi-optimal selection of the mother wavelet at each level of SNR. While the performance of time-frequency CNNs has been attempted in literature for other radio frequency classification problems such as radio frequency fingerprinting, such an extensive study for the problem of channel identification is novel to the best knowledge of the authors, especially with the application of the adaptive method. Future developments will investigate other data sets to investigate the generalization of the approach proposed in this paper. In addition, other TFTs based on the synchrosqueezing concept will also be explored. Conceptualization, G.B. and F.B. ; Methodology, G.B. ; Resources, F.B. ; Data Curation, G.B. and F.B. ; writing G.B. and F.B. All authors have read and agreed to the published version of the manuscript. No specific funding was used for this study. Not applicable. The study did not involve humans. The authors have started the approval process inside their organization to make the data set public. Until the process is completed, the data set is available on request to the authors. We acknowledge the contribution of the colleagues [PERSON] and [PERSON] of the JRC for the technical support in the setup of the laboratory. The authors declare no conflict of interest  
The following abbreviations are used in this manuscript: