Maximum likelihood estimator for velocity estimation in HetNets based on handoff count

In this paper, we propose a maximum likelihood based estimation technique for accurately estimating the velocity of mobile users in Heterogeneous networks (HetNets). In HetNets, base station (BS) density around a particular user is more compared to the traditional cellular network, resulting in frequent handoffs for a better quality of service. However, if the mobility management is not efficient, there is always a high probability of handover failures, unnecessary handoffs and call drops. The accurate estimation of the velocity of mobile users is one of the most challenging task in mobility management. The proposed velocity estimation strategy is based on handoff count which occurs during a predefined time span. Here we model densely deployed BSs using random waypoint process (RWP) and analyse the statistics of handover count as a function of velocity, BS density, and time span. Using these statistics we first derive the Cramer-Rao lower bound (CRLB) and later we determine a maximum likelihood estimator (MLE), which is an asymptotic unbiased estimator. We validate our approach by simulation which show the tight closeness of MLE asymptotic variance with CRLB. In addition, our result illustrates that velocity estimation error decreases with increase in BS density and time span of handover count measurements.