## 4 Experiments and Results
### Results and Discussion for Protocol Classification  
Our experiment aims to measure the effectiveness of using CDAE compressed features as an input for a classifier to identify the unlicensed RATs, studying the performance of different Artificial Neural Network classifier architectures in identifying the protocols under different conditions. The different classifier configurations are summarized in Table 2. They are distinguished based on the number of layers, the use of convolutional or full connectivity layers (CNNs as opposed to ordinary _Multi-Layer Perceptrons_ (MLPs)), and most importantly by the fact that they receive as inputs the original noisy images, or their compressed representation coming from our previously trained CDAE (this option is described in Table 2 as CDAE weights). Notice that the structure of the input to the classifier changes depending on the use of the original noisy image or the CDAE representation: in the former case, the input size is \((128,128,1)\), i.e., \(128\times 128\) a Boolean image of depth 1; in the latter case, it is \((4,4,32)\), i.e., a set of 32 Boolean images each of size \(4\times 4\) (from the CDAE structure, one can already see that we imposed that the network learns 32 filters). Notice also that the input's former representation has \(2^{14}\) Boolean degrees of freedom, while our CDAE filter-based representation of the input only uses \(2^{9}\) Boolean degrees of freedom. Figure 6: The spectrograms of five unlicensed radio signal for noise _scenario 1_ with SNR 0 dB. Shown above: (**a**) the noisy spectrograms, (**b**) the clean spectrograms, and (**c**) the decoded spectrograms. The solution we put forward in this paper is reported in the last row of the table: it consists of a 24-layer CNN classifier receiving as input our CDAE representation. This pipeline outperforms other classifiers in the identification of the protocols (IEEE 802.11a, IEEE 802.11n, IEEE 802.11 ax, IEEE 802.11 ac, and unlicensed LTE) even under severe signal degradation (SNR is 0 dB) and for the different channel conditions (severe fading, multipath, NLOS, Doppler Shift, and all the previously described channel conditions). Moreover, most alternative models display a performance comparable to that of a random classifier (which, with 5 alternative classes, would display a 20% accuracy). A more detailed description follows. The 4-layer MLP with and without CDAE, the 6-layer CNN classifier with and without CDAE, the 8-layer CNN classifier with and without CDAE, and the 13-layer CNN classifier without CDAE are stuck at a 20% accuracy across the whole considered range of channel scenarios and SNR levels. Overall, they perform as a random classifier. The CDAE representation with 24 layers, on the contrary, features an accuracy ranging from 60% to 100% for multiple protocol classification, depending on the noise and SNR values. CNN3 + AE performing better than CNN2 + AE suggests that adding one CNN layer helps to extract more features, which are useful for identification. Finding the dimensionality of the CNN which corresponds to the coarseness of data representation where the \"right\" features emerge is a classic problem, part of the CNN model's hyperparameter tuning. We want to highlight that we explored both a lower number of layers and a higher number of layers. As it often happens, there is an optimal point in the complexity of a network that represents a good trade-off between bias and variance: the CNN3 + AE represents such a good trade-off. On the contrary, we found that the CNN2 + AE performance does not generalize well across all the datasets, while already the CNN4 + AE shows a clear sign of overfitting: we assume that increasing the number of layers thus further increasing the number of parameters would produce an even more apparent overfitting. Therefore, we did not experiment with CNN5 + AE, CNN6 + AE, and so on.