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

Abstract

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.