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In wireless cellular networks with densely deployed base stations, knowingthe velocities of mobile devices is a key to avoid call drops and improve thequality of service to the user equipments (UEs). A simple and efficient way toestimate a UE's velocity is by counting the number of handovers made by the UEduring a predefined time window. Indeed, handover-count based mobility statedetection has been standardized since Long Term Evolution (LTE) Release-8specifications. The increasing density of small cells in wireless networks canhelp in accurate estimation of velocity and mobility state of a UE. In thispaper, we model densely deployed small cells using stochastic geometry, andthen analyze the statistics of the number of handovers as a function of UEvelocity, small-cell density, and handover count measurement time window. Usingthese statistics, we derive approximations to the Cramer-Rao lower bound (CRLB)for the velocity estimate of a UE. Also, we determine a minimum varianceunbiased (MVU) velocity estimator whose variance tightly matches with the CRLB.Using this velocity estimator, we formulate the problem of detecting themobility state of a UE as low, medium, or high-mobility, as in LTEspecifications. Subsequently, we derive the probability of correctly detectingthe mobility state of a UE. Finally, we evaluate the accuracy of the velocityestimator under more realistic scenarios such as clustered deployment of smallcells, random way point (RWP) mobility model for UEs, and variable UE velocity.Our analysis shows that the accuracy of velocity estimation and mobility statedetection increases with increasing small cell density and with increasinghandover count measurement time window.

Handover Count Based Velocity Estimation and Mobility State Detection in Dense HetNets