## 7 Conclusions  
This paper is based on user experience for real-time video conference service and combines deep learning to implement a handover mechanism by predicting MOS to enhance the quality of service for users in the LTE network. First, we summarize the MOS evaluation criteria of a real-time video conference service and establish a handover mechanism based on MOS.  
In addition, we have proposed a deep learning model to predict the RSRP of LTE signal strength, and we analyzed the prediction accuracy of four deep learning models, including MLP, CNN, RNN and LSTM. From the evaluation experiments of the prediction results of the four deep learning models, it can be observed that all the deep learning models lead to a decrease in accuracy when predicting longer time intervals. However, the RNN model has the lowest MSE when predicting after 2, 4, 6, 8 and 10 s, respectively. Moreover, the experimental results for model loss indicate that the RNN model has better prediction results in both the short and long term than the other three deep learning models.  
Based on the predicted RSRP values from the proposed RNN deep learning model, the corresponding MOS values are calculated according to Equation (7). The experimental results have also demonstrated that the proposed deep-learning-based handover mechanism can improve the quality of users' real-time video conference service experience as compared with the general LTE event A3 handover mechanism. The MOS values equal to or greater than 4 for the proposed deep-learning-based handover mechanism can accumulate about 682 s; however, it can accumulate only about 673 s for the LTE event A3 handoff mechanism.  
Furthermore, this paper focuses on the proposed deep learning handover mechanism via the Skype video conference services in LTE. However, the same approach could be extended to the main applications of online video conferencing services such as MS Teams, Zoom, Google Meet, etc., by adjusting the parameters.  
In addition, AGVs and UAVs are two of the major applications of current mobile robotics in the context of Industry 4.0. For example, mobile robots can implement applications such as image recognition and object detection via real-time video streaming. However, the proposed deep-learning-based handover mechanism can provide a more stable real-time video streaming service while the robot is moving to meet the video quality requirements for image recognition and object recognition.  
In the future, the proposed deep-learning-based handover mechanism can be extended to different application services for MOS handover mechanism research and can be implemented in the 5G next-generation network.  
Conceptualization, T.-H.L., L.-H.C. and Y.-S.C.; methodology, T.-H.L., L.-H.C. and Y.-S.C.; software, Y.-S.C.; validation, T.-H.L., L.-H.C. and Y.-S.C.; formal analysis, T.-H.L., L.-H.C. and Y.-S.C.; investigation, T.-H.L. and L.-H.C.; resources, T.-H.L.; data curation, T.-H.L., L.-H.C. and Y.-S.C.; writing--original draft preparation, T.-H.L., L.-H.C. and Y.-S.C.; writing--review and editing, T.-H.L. and L.-H.C.; visualization, Y.-S.C.; supervision, T.-H.L.; project administration, T.-H.L.; funding acquisition, T.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, ROC, grant number MOST 109-2221-E-142-002 and MOST 110-2221-E-142-001.  
The authors declare no conflict of interest.