## 5 Experiments and Discussion
### Experiment Settings  
Firstly, we constrain the weights to be optimized in the feature extraction network so that they fall in the range of \(-1\) to \(1\), which also limits the search range of the differential evolution algorithm. Then, in the first stage of model training, the learning rate of the SGD optimization method is set as \(0.01\). Furthermore, in the feature extraction network, there are two parameters that can directly affect the effectiveness of feature extraction: the length and number of shapelets, assuming that the length of shapelets is \(M\) and the number of shapelets is \(K\). To find the appropriate parameter values, we conducted a grid search on \(M\in\{50,100,150,200,250,300,350,400,450,500\}\) and \(K\in\{3,4,5,6,7,8,9,10\}\). Due to the fact that the F1 score can comprehensively consider precision and recall, which are important evaluation metrics in detecting LTE-R cells with degraded communication performance [3], the F1 score is used as the evaluation metric for the grid search. When conducting the grid search, an SVM is used to classify the training set after feature extraction. The average F1 score obtained from a 5-fold cross-validation is taken as the evaluation result for each search. The grid search results for \(M\) and \(K\) are shown in Figure 7.  
It can be seen from Figure 7 that when \(M\) equals \(300\) and \(K\) equals \(5\), we can obtain the maximum F1 score value. Therefore, in this experiment, we set the length of the shapelets \(M\) to \(300\) and the number of shapelets to \(5\).  
After determining the length and number of shapelets, it is necessary to determine the relevant parameters of the differential evolution algorithm in order to further ensure the performance of the detection method, i.e., the maximum iteration \(G\) and the population \(NP\). Generally speaking, the larger the population size and maximum generation, the better the optimization effect. However, increasing \(G\) and \(NP\) can also lead to increased computational complexity, which affects algorithm efficiency. To find appropriate parameter values, we conduct a grid search on \(G\in\{0,40,60,80,100\}\) and \(NP\in\{10,20,30,40,50\}\). Similarly, we conduct a grid search and utilize an SVM to classify the training set after feature extraction. The average F1 score obtained from a 5-fold cross-validation is taken as  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Properties** & **Property Values** \\ \hline Sequence length & 500 \\ Dataset size & 2554 \\ Normal data size & 2470 \\ Abnormal data size & 84 \\ Training set size & 1788 \\ Test set size & 766 \\ Imbalance ratio & 29.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The detailed information of the degradation detection dataset.  
Figure 7: The grid search results of \(M\) and \(K\). (**a**) shows the grid search results for \(K\) and \(M\) in the form of a heat map, and the darker the color, the higher the F1 score. (**b**) shows the grid search results for \(K\) and \(M\) in the form of a group bar chart, and the longer the length of the bar, the higher the F1 score.  
the evaluation result for each search. The grid search results for \(G\) and \(NP\) are shown in Figure 8.  
As can be seen from Figure 8, when \(NP\) equals 30 and \(G\) equals 60, we can obtain the maximum F1 score. Therefore, in this experiment, we set the population to 30 and the maximum iteration to 60.  
All the codes of our approach and comparison methods are written in Python 3.6. All the experiments were carried out on a laptop, which was configured as follows: CPU is i5-8400 (Intel, Santa Clara, CA, USA), memory is 8G, and the graphics card is NIVIDA 1050 Ti 4G (NIVIDA, Santa Clara, CA, USA).