Stochastic Geometry and User History Based Mobility Estimation

5G is envisioned to support scalable networks and improved user experience with virtually zero latency and ultra broadband service. Supporting mobility is one of the key issues and also for network resource utilization efficiency. In this paper, we will focus on mobility management and user equipment (UE) speed class estimation, also known as Mobility State Estimation (MSE). We will study UE History Information standardized in 3GPP and investigate the impact of the environment and UE trajectory in estimating UE speed. We evaluate the effectiveness of our approach in comparison to several existing methods using realistic mobility trace and network topology provided by Kolntrace project. Results show that the UE speed estimation performance of legacy LTE MSE procedure can be significantly enhanced.