## 2 Literature Review  
The use of DL for channel identification is relatively recent, and there are not many studies investigating this approach. On the other hand, it is a fast-growing field of research. This section aims to provide an overview of the studies available in the literature with a specific focus on the application of CNN. In [15], the authors have investigated the application of CNN to the impulse response of an 802.15.3a UWB in four different types of channel environments: LOS (0-4 m), NLOS (0-4 m), NLOS (4-10 m) and extreme NLOS. The 802.15.3a UWB signal was generated using MATLAB. The STFT was used to transform the 802.15.3a UWB signal to a time-frequency representation (i.e., with the spectrogram), which is then fed to the CNN. In comparison to the study in [15], this study has investigated the application of different TFTs beyond STFT. In [11], the authors have used CNN in combination with a time-frequency representation called power-angle spectrum (PAS) to distinguish between LOS and NLOS (thus a binary classification). The authors have used public measurement data of a channel sounder in an urban scenario. The results obtained with the PAS and CNN combination are compared with SVM and Decision Tree algorithms in addition to classical models of Ricean Fading. The results show that PAS and CNN are able to outperform significantly the \"shallow machine learning\" algorithms such as SVM and DT and, even more, the classical model. In comparison to [11], the authors have investigated more than two propagation conditions (i.e., LOS and NLOS), and they have investigated the use of different time-frequency representations. In [16], the authors have proposed an NLOS detection approach that uses convolutional neural networks (CNNs) directly on the channel impulse responses (CIRs) from UWB-based radio systems. The authors have evaluated the application of CNN on a data set created by themselves in a realistic setting (industrial manufacturing plant). The CNN is applied directly to the magnitude of the CIR (thus is a 1D CNN). The results using CNN are compared against the results obtained with SVM (with a set of statistical features), and CNN significantly outperforms SVM. In comparison to [16], the authors of this study have used various time-frequency distributions in combination with CNN rather than the one-dimensional magnitude. In fact, the results shown in this study demonstrate that the time-frequency distributions with CNN have superior performance than the approach based on one dimension with CNN. In addition, this study analyzes more fading conditions than just the binary LOS/NLOS in [16]. In [17], the authors have applied CNN in the specific context of channel estimation for drones. In particular, the authors of [17] have estimated the LOS/NLOS conditions for Public Safety Networks (PSN) using Physical Random Access CHannel (PRACH) waveforms, which are used during the discovery phase in wireless cellular systems. The LTE PRACH signal is generated using MATLAB, and it is reshaped in the form of a matrix where one dimension is time, and the other is the concatenation of the antenna outputs; this matrix is then treated as a one-channel image for the input of the CNN. Only the LOS and NLOS cases are considered in the study, and the CNN performs a binary classification. In comparison to [17], this study considers more complex fading scenarios than the binary case of LOS/NLOS. In addition, various time-frequency distributions are evaluated as input to the CNN. One example where CWT was used in combination with CNN for LOS/NLOS channel identification to improve indoor positioning in UWB technology is in [12] where the Morlet wavelet was used. In comparison to [12], this study has investigated other mother wavelets in addition to Morlet and has investigated more propagation contexts beyond LOS/NLOS for a more general application rather than indoor positioning alone.