Mobility Awareness in Cellular Networks to Support Service Continuity in Vehicular Users

Mobile communication is an ubiquitously used technology that has evolved through various generations and is currently on the verge of its fifth generation (5G). In the recent years, Intelligent Transportation Systems (ITS) and supplementary vehicular use cases (e.g., autonomous driving) are considered widely within the scope of cellular networks. These use cases generally demand reliable and low latency services from the cellular network. Mobile Edge Clouds (MEC) in the 5G networks are often applicable to satiate such service demands of a vehicular user. However, the cellular handovers (HO) of vehicular users prompt frequent service migration among the MECs. The handovers and service migration increase the service interruption of a user. In this paper, we consider machine learning (ML) based mobility awareness to obtain future service migration and HO sites of a user. This enables smooth service migration by allowing non-state data transfer earlier to user handover. Further, it provides sufficient time for establishment of successful Coordinated Multipoint (CoMP) transmissions, which will reduce service interruption due to HO. Simulation results show that the proposed framework provides timely assistance for service migration and significantly reduces the service interruption time.