User velocity estimation in HetNets utilizing vertical and horizontal handover counts via maximum likelihood estimation

In this paper, we propose a maximum likelihood estimation (MLE) of user velocity based on Rayleigh distributed handover counts in heterogeneous networks (HetNets). The high density of base stations (BSs) in HetNets leads to mobile users experiencing unnecessary handovers and service outages, emphasizing the importance of mobility management in HetNets. Since user velocity plays an important role in the handover process, its knowledge is necessary for efficient mobility management. Our proposed velocity estimation strategy involves tracking of both vertical and horizontal handovers over a defined period. We utilized mathematical modeling to estimate that the probability mass function (PMF) of the handover counts in the HetNets scenario follows a Rayleigh distribution. We determined the scale parameter by analyzing the close deployment of small cells and macro-cell BSs using stochastic geometry. Our approach assumes a random distribution of BSs based on a homogeneous Poisson point process and investigates the handover-count PMF considering factors such as user velocity, BS density, and the duration of handover-count measurements. Using these statistics, we derived the Cramer-Rao lower bound (CRLB) and applied a MLE technique to estimate user velocity. The MATLAB simulation method is used to validate our approach for the velocity estimation, and the results show the tight closeness of MLE asymptotic variance with CRLB. The numerical analysis indicates that as user velocity increases, the variance of the estimation also increases. Conversely, the simulation results demonstrate that as BS density and the time span for handover-count measurements increase, the velocity estimation error decreases.