## 4 Experimental and Analysis
### Performance of the Algorithm on the Generated Dataset  
After updating the dataset, the classification categories of the neural network were nearly doubled. The expanded data were pre-processed to verify the performance of the DL-based SNR evaluation algorithm. Then, the data are fed into the neural network and compared to the classification results before the update. The 2D-CNN is a traditional model which mainly refers to the classical LeNet-5 convolution neural network. The model is mainly composed of the first half of the convolution module and the second half of the fully connected module, showing the advantages of lightweight compared with the existing deep learning model [27]. The model structure of 2D-CNN is shown in Figure 7. To analyze the performance of proposed model on the generated dataset, the mean absolute error (MAE) and mean relative error (MRE) are utilized to evaluate the training error. MAE evaluates the deviation degree between the real value and the predicted value, the smaller MAE is, the better the model quality is and the more accurate the prediction will be. MRE represents the average ratio between the absolute error and the true value, reflecting the reliability of the measurement. The definition of MAE and MRE are denoted as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|\hat{y}_{i}-y_{i}|, \tag{4}\]  
where \(n\) is the number of samples, \(y_{i}\) denote the true result and \(\hat{y}_{i}\) describe the predicted result. Similarly, the mean relative error (MRE) is denoted as:  
\[\text{MRE}=\frac{1}{n}\sum_{i=1}^{n}\frac{|\hat{y}_{i}-y_{i}|}{y_{i}}. \tag{5}\]  
Table 2 shows a comparison of the evaluation metric values for the three algorithms. It can be seen that although the performance of the baseline model is reduced, it still achieves good results compared to the two existing algorithms. The average absolute error of the 2D-CNN results decreases with the addition of data due to the smaller interval between categories and the smaller gap between neighboring SNR. In detail, the smaller SNR reduces the average error but also increases the classification uncertainty of the neural network due to the increased number of categories and the smaller gap.  
Figure 6: Comparison of datasets before and after update.  
Figure 7: The model structure of 2D-CNN [27].  
The performance of mean absolute error and mean relative error of the proposed TP-CNN and the baseline methods are shown in Figures 10 and 11, respectively. Regarding the average absolute error of the algorithms, TP-CNN is reduced by approximately 1/3 compared to 2D-CNN and about five times compared to the better performance of the two mathematically driven algorithms. In terms of the average relative error of the algorithm, TP-CNN is almost 0.01 less than 2D-CNN and ten times less than the conventional algorithm. These results show that the new proposed TP-CNN performs better on the dataset and has a greater performance advantage, which is supported by the 92.16% accuracy of the test set on the proposed two-way model.  
Figure 10: Mean absolute error of the four algorithms for SNR evaluation.  
Figure 9: Relative error of four algorithms evaluated at different SNRs.