## 2 Related Work
### Related Work on Neural Networks and Deep Learning  
The authors in [8] adopted a CNN (as in this paper) to classify different types of jamming signals present in GNSS bands. In particular, pulse jamming, narrowband, and wideband chirp jamming were used to generate the jamming signals. As in this study, different power levels were also used. The main difference of this paper with [8] is that the analysis with CNNs is performed directly on the radio frequency space in [8], while in this study the CNR values are used. The approach in this paper is more realistic because the classification of the interferences in the original radio frequency space would require an additional spectrum analyzer component, while the CNR values (as \(C/N_{0}\) time series) are provided even by consumer-grade GNSS receivers. Apart from this significant difference, the authors of [8] do not take into consideration the impact of the bit-depth. The authors of [9] have also used a CNN with a similar limitation that the DL algorithm is applied directly to the spectrograms of the combined GNSS and jamming signals, which can also be computing-intensive due to the need to generate the spectrograms from the signal in space. The jamming signals used in [9] are chirp-based, pulse, or narrowband signals, which are similar to the ones used in this paper. The results of [9] show that SVM achieves better accuracy than the CNN, whereas this study obtained opposite results. Furthermore, it should be taken in consideration that this study used a more sophisticated CNN architecture with a multi-head attention layer rather than the baseline two-layer CNN architecture proposed in [9]. A similar approach was also followed in [10] with a CNN architecture and [11], where DL algorithms (i.e., ResNet18 and Transformers) were applied both to the time domain and spectral domain representation of the combination of the GNSS signal and the interferences. Pulse, chirp, and modulated signals were used to generate the interference. An evolution of these approaches is to use combinations of time frequency transforms as in [12], where the Wigner-Ville transform (WVT) and the spectrograms were used in combination to identify and classify interferences in GNSSs using a CNN. The studies [10; 11; 12] have the same limitation as the previous papers because the application of the CNN to the original signal or its spectral domain (either with Fast Fourier Transform (FFT) or with Time Frequency (TF)) does not take into consideration the processing (e.g., signal correlation) of the GNSS receiver and it would require additional components. The bit-depth aspect was also not considered. A similar use of spectrogram and WVT and CNN was adopted in [13], while the authors of [3] applied more sophisticated DL algorithms and architectures than a vanilla CNN for the classification of interference, but again the spectrogram was used and no analysis on bit-depth was performed. Regarding the use of \(C/N_{0}\) instead of spectrograms, the authors in [14] analyzed the impact of jamming signals with different levels of power on the \(C/N_{0}\) but the bit-depth was not considered and no classification attempt with ML or DL was made. We summarize in the following bullet list the key contributions and advancements of this paper in comparison to the reviewed literature:  
* With a good degree of novelty, the CNN is applied to the \(C/N_{0}\) digital artifacts created by the GNSS receiver instead of the spectral domain representation of the signal in space. Even if the application of the CNN to the \(C/N_{0}\) observables may be more challenging because the original Radio Frequency (RF) signal is pre-processed and some information could be lost, the approach is more practical and realistic because it relies only on the output of the GNSS receiver and no additional components (e.g., spectrum analyzer) are needed.