## 6 Experimental Results and Analysis  
In this section, the proposed deep-learning-based handover mechanism is evaluated to meet the MOS requirements of the real-time video conference service. We also compare the original event A3 and the proposed deep-learning-based handover mechanism. The experiment topology and UE's moving path are shown in Figure 20. The injection of network traffic from the iperf tool is used to simulate the transmission of a video call in Skype. The experiments were performed three times with fixed paths for the original event A3 and the proposed deep-learning-based handover mechanism individually. The mobile nodes were evaluated with the same moving speed. According to the previous experimental results, the RNN model has the lowest MSE value and the highest R-square value than the MLP, CNN and LSTM models. Thus, the RNN deep learning model is implemented using the Mininet-WiFi for LTE network simulation. The acceptable threshold of MOS is set to 4 since the speed of the mobile node in the experiment is 20 m per second. The eNB will make RSRP predictions for the next 2 s to confirm whether the eNB can meet the required MOS for Skype video conferencing services. If not, eNB activates the handover mechanism before the signal quality is degraded to avoid an MOS value lower than 4. Figures 21 and 22 show the packet loss rates of three experimental results of the proposed deep-learning-based handover mechanism and LTE event A3 handover mechanism, respectively. The LTE event A3 handover mechanism is based only on the signal strength difference between eNBs. It results in a higher packet loss rate under the required network traffic for the real-time video conference service due to the late triggering of the handover mechanism, as compared to the proposed deep-learning-based handover mechanism. Therefore, the overall packet loss rate of the event A3 handover mechanism is relatively higher than that of the proposed deep-learning-based handover mechanism. Figure 20: UE’s moving path for handover mechanism testing. Figures 23 and 24 show the 95% confidence interval of three experimental results of packet loss rates for the proposed deep-learning-based handover mechanism and the LTE event A3 handover mechanism, respectively. Within the 95% confidence interval, it can be found that both the event A3 handover mechanism and the proposed deep-learning-based handover mechanism can maintain their packet loss rates below 4%. The highest packet loss rates of the proposed deep-learning-based handover mechanism is from 61% to 62%; however, the highest packet loss rates of the LTE Event A3 handover mechanism is from 92% to 97%. Figure 21: The packet loss rates of three experimental results of the proposed deep-learning-based handover mechanism. Figures 25 and 26 show the MOS of three experimental results of the proposed deep-learning-based handover mechanism and LTE event A3 handover mechanism, respectively. We observe the experiments of the MOS values equal to or greater than 4 for both mechanisms. In the three experiments, the proposed deep-learning-based handover mechanism with MOS values equal to or greater than 4 are maintained for 682, 683 and 682 s. However, the MOS values equal to or greater than 4 for the three experimental results of the LTE event A3 handover mechanism are only 673, 674 and 673 s.  
Figure 22: The packet loss rates of three experimental results of the LTE event A3 handover mechanism. Figure 23: The confidence interval of packet loss rates of the three experimental results for the proposed deep-learning-based handover mechanism. Figure 24: The confidence interval of packet loss rates of three experimental results for the LTE event A3 handover mechanism. Figure 25: The MOS of three experimental results of the proposed deep-learning-based handover mechanism. Figure 27: The confidence interval of MOS of the three experimental results for the proposed deep-learning-based handover mechanism.