In this category, both DL and 'Shallow Machine Learning' (e.g., KNN, decision tree, and support vector machines) have been used [1]. Even if DL requires significant computing resources in comparison to shallow ML, DL has demonstrated superior performance for classification problems in the context of wireless communications [2]. Then, this paper evaluates the application of Deep Learning and specifically CNN to the problem of channel identification. CNN is used in this context because it has demonstrated a superior classification capability for image processing [3] problems. To exploit the classification capabilities of the CNN, we transform the time series to image through TFT and distributions such as CWTs, spectrogram, and Wigner Ville distribution. The selection of these TFTs is based on the positive results in the literature on similar classification problems in wireless communication literature, including radio frequency fingerprinting [4], modulation classification [5], and spectrum monitoring of radar signal [6]. A more extensive analysis of the application to wireless communication is presented in the Literature Review Section 2. In this study, we consider blind channel identification, where the evaluation of the channel responses is based only on the channel outputs without the use of training signals. We performed an analysis of the different TFTs and parameters of the CNN applied on a data set composed of pulse channel responses based on weather radar signals with different fading channel conditions. The fading channel conditions have been created using the channel emulator in the radio frequency laboratory of the authors. In particular, we have reproduced 3 GPP-like fading models based on different Tapped-Delay Line (TDL) settings with Nakamami-m fading. We have used 3 GPP fading models as an inspiration because it is commonly used in wireless communication problems for channel estimation and prediction [7; 8; 9] and it is well defined in the 3 GPP standards. In addition, AWGN is added to the signals to simulate different values of SNR in dB. The results show that the choice of the TFTs is quite important to obtain optimal channel identification results and that different optimal transforms are obtained for each data set. The application of TFTs together with CNN was shown to produce an effective classification performance in many wireless communication problems in comparison to the direct application of the CNN to the original raw data (RF fingerprinting in [4], spectrum sensing in [10], channel fading classification in [11; 12]). The potential reason for this phenomenon is that by means of time-frequency analysis, the source signal is transformed into a map in the time and frequency domains, which fully illustrates the variations of the signal energy variation w.r.t. time and frequency [13]. In particular, the results presented in this paper show that the CWT with different mother wavelets provides the best performance in terms of accuracy among the different time-frequency representations for different values of SNR in dB. On the other hand, the choice of the mother wavelet remains a challenge because different mother wavelets offer different performances for the various values of SNR. The optimization of the optimal mother wavelet could be computing demanding using approaches where the CNN classification has to be executed for each mother wavelet as in [14]. In some cases, authors select a specific wavelet in combination with CNN as in [12], where the Morlet wavelet is used for a similar problem, but then the opportunity to select the optimal wavelet is waived. Then, this paper proposes a novel approach where the mother wavelet is chosen in a pre-processing step based on feature extraction and shallow machine learning algorithms, which allows the selection of the optimal or quasi-optimal mother wavelet for the application of CNN. The structure of this paper is the following: Section 2 provides a review of the relevant literature in this field.