Handover Count Based UE Velocity Estimation in Hyper-Dense Heterogeneous Wireless Networks

In wireless cellular networks with densely deployed base stations, knowing the velocities of the user equipments (UEs) is a key for efficient mobility management. A simple and efficient way to estimate a UE´s velocity is by counting the number of handovers made by the UE during a predefined time window. Indeed, handover-count based mobility state detection has been standardized since Long Term Evolution (LTE) Release-8 specifications. The increasing density of small cells in wireless networks is advantageous, as it can help in accurate estimation of velocity and mobility state of a UE. In this paper, we model densely deployed small cells using stochastic geometry, and derive an approximation to the probability mass function of handover count as a function of UE velocity, small cell density, and time interval of handover count measurement. Then we derive Cramer-Rao lower bound (CRLB) for the velocity estimate of a UE, and also provide an unbiased estimator for the UE´s velocity. Our analysis shows that the accuracy of velocity estimation increases with increasing small cell density and with increasing time interval of handover count measurement.