## 4 Handover Dataset Collection and Deep Learning Model Performance Analysis
### Deep Learning Model Evaluation  
Since regression models are used to predict numerical data with continuous values, the way to evaluate a regression model is different from the common categorical model evaluation. The common regression model evaluation methods are mean square error (MSE) [30] and coefficient of determination (R-squared) [31]. The MSE is calculated as in Equation (10).  
\[\text{MSE}\ =\ \frac{1}{n}\sum_{i\ =\ 1}^{n}\left(Y_{i\_original}-Y_{i\_ predit}\right)^{2} \tag{10}\]  
In Equation (10), \(n\) represents the total number of test samples used for evaluation, i represents the first sample, \(Y_{i\_original}\) represents the original data of the ith test sample, and \(Y_{i\_predit}\) represents the predicted result of the ith test sample. The MSE is inversely proportional to the predicted result. The smaller value of MSE means that the predicted result is closer to the actual value.  
R-squared represents the ratio between the variance of the predicted data and the variance of the actual data in the deep learning model. A higher value of R-squared indicates a higher correlation between the predicted and actual data. R-squared is calculated as in Equation (11).  
\[\text{R\_squared}\ =\ \frac{\text{SSR}}{\text{SST}} \tag{11}\]  
In Equation (11), SSR represents the amount of variation in the regression model and is calculated as in Equation (12).  
\[\text{SSR}\ =\ \sum_{i\ =\ 1}^{n}\left(Y_{i\_predit}-\overline{Y}\right)^{2} \tag{12}\]  
where \(\overline{Y}\) represents the mean of the actual data (\(Y_{i\_original}\))  
In addition, SST represents the sum of squares of the differences between the original data and the actual data, and is calculated as shown in Equation (13).  
\[\text{SST}\ =\ \sum_{i\ =\ 1}^{n}\left(Y_{i\_original}-\overline{Y}\right)^{2} \tag{13}\]  
Figure 9 shows the model loss of RSRP after 2 s predicted by the four deep learning models, MLP, CNN, RNN, LSTM models, respectively. Similarly, Figures 10-13 show the model loss of RSTP after 4, 6, 8, and 10 s, respectively. The loss function used in the training is MSE. The MSE is chosen such that the deep learning models can reduce the gap between the predicted and the actual data during the training process. As shown in Figures 9-13, the RNN model has lower MSE values than MLP, CNN and LSTM models after 2, 4, 6, 8,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Epoch & 100 \\ \hline Batch size & 50 \\ \hline Activation & relu \\ \hline Optimizer & adam \\ \hline Loss function & MSE \\ \hline \hline \end{tabular}
\end{table}
Table 7: Deep learning training parameters.  
and 10 s. That is, the RNN model has better prediction performance of RSRP than other models for all different predicting time intervals.  
Figure 9: The model loss of RSRP after 2 s of prediction: (**a**) MLP, (**b**) CNN, (**c**) RNN, (**d**) LSTM.  
Figure 10: The model loss of RSRP after 4 s of prediction: (**a**) MLP, (**b**) CNN, (**c**) RNN, (**d**) LSTM.  
Figure 11: The model loss of RSRP after 6 s of prediction: (**a**) MLP, (**b**) CNN, (**c**) RNN, (**d**) LSTM.  
Figure 12: The model loss of RSRP after 8 s of prediction: (**a**) MLP, (**b**) CNN, (**c**) RNN, (**d**) LSTM.