An Intelligent Handover Mechanism Based on MOS Predictions for Real-Time Video Conference Services in Mobile Networks

Abstract

In mobile networks, handover mechanisms provide fast and smooth access service for mobile users. However, one of the main challenges in mobile networks is the handover management with increased mobility and bandwidth demand of the required network services. Therefore, in this paper, we propose a MOS-aware (mean opinion score-aware) mobile network handover mechanism based on deep learning to determine the appropriate handover time for real-time video conference services in mobile networks. We construct a wireless network topology with LTE characteristics in a Mininet-WiFi simulation. User equipment (UE) can determine the service-required MOS (Mean Opinion Score) from the proposed deep-learning-based handover mechanism with appropriate handover time. Simulation results show that the proposed scheme provides higher performance than the original A3 handover mechanism. The contribution of this paper is to combine the real-time video conferencing services with a deep-learning-based handover mechanism by predicting MOS values to improve the quality of service for users in mobile networks.