## 5 The Proposed Deep-Learning-Based Handover Mechanism for Real-Time Video Conference Services  
Figure 14 shows the mobile moving path for a deep learning model performance analysis. The purpose of designing this verification path is to observe whether the deep learning model can maintain a certain prediction accuracy for the RSRP after changing the movement direction. During the collection of the verification dataset, the UE starts moving from a position close to the center of the eNB and changes its direction of movement when it reaches the edge of the effective transmission range of the eNB at 202 s. The change of  
Figure 13: The model loss of RSRP after 10 s of prediction: (**a**) MLP, (**b**) CNN, (**c**) RNN, (**d**) LSTM. RSRP is recorded once per second during the movement. The experiment collects a total of 525 s of verification dataset. The test results of the validation dataset are presented in this section. The prediction results of four deep learning models: MLP, CNN, RNN, and LSTM, are implemented to analyze the RSRP prediction function for LTE and to select the deep learning model with best prediction capability. In order to verify the reliability of the prediction models, we analyze the results of the deep learning models in predicting the RSRP values at different future time points: the 2 nd, 4 th, 6 th, 8 th and 10 th seconds, with an interval of 2 s by using MLP, CNN, RNN and LSTM deep learning models. The 2 s period is chosen as the shortest prediction time. The main reason is that when the deep learning model predicts the future RSRP and prepares handover based on the prediction results, the process requires some processing and communication time. If the interval time is set to 1 s, it may result in a situation where the handover event has already occurred by the time the prediction result is returned. Therefore, using 2 s as the prediction time can provide the eNB with enough response time to analyze and process the prediction results. The purpose of selecting and predicting RSRP from eNBs with different time intervals is to observe whether the selected eNBs can continue to satisfy the users' demand, thus avoiding the ping-pong effect at the handover. In addition, by predicting the selection of eNBs that can maintain MOS greater than 4 for a longer period of time, frequent handover will be avoided. The speed of UE movement in the experiment is set at 90 km/h. The maximum prediction time is 10 s. In this case, the UE moves a distance of 250 m in 10 s. In addition, when the user wanders for a long time around the cell boundary, the proposed mechanism in this paper will predict and select the RSRP with different time intervals for eNBs to continually satisfy the user's requirements in order to avoid the ping-pong effect during handover. Based on the prediction results of the four deep learning models shown in Tables 8-11, we can observe that the mean square error (MSE) of accuracy gradually increases and the R-square value of accuracy slightly decreases as the prediction interval increases. The results indicate that the models are less accurate in predicting longer intervals. Among the four deep learning models, the minimum MSE value of accuracy is the RNN model with 0.111 and the maximum R-square value of accuracy is the RNN with 0.999 compared with  
Figure 14: Mobile moving path for deep learning model performance analysis. the other three models in the 2 s period. This indicates that RNN has better performance results than the other three models. Figures 15-19 show the RSRP results predicted by the four deep learning models after 2, 4, 6, 8, and 10 s. The \(X\)-axis shows the time in seconds when the UE moves, and the \(Y\)-axis shows the change in RSRP in dBm. The blue line represents the actual RSRP, and the red line is the predicted RSRP from the trained deep learning model. A higher overlap between the blue and red lines means that the prediction result of the deep learning model is better.