An enhanced approach to minimum variance unbiased velocity estimation, incorporating horizontal and vertical handoff in HetNets

Abstract

Abstract This paper presents an enhanced approach to Minimum Variance Unbiased (MVU) velocity estimation in Heterogeneous-Networks (HetNets) by addressing horizontal and vertical handoffs. In HetNets, the abundance of base stations (BSs) results in frequent unnecessary handoffs and service disruptions for mobile users, posing challenges for mobility management. Accurate velocity estimation is crucial for effective mobility management. Our proposed strategy involves tracking vertical and horizontal handoffs over a specified time interval. Through mathematical modeling, we approximate the analytical expression of the handover count probability-mass-function in HetNets as Rayleigh distributed and calculate its scale parameter based on velocity, BS density, and measurement time span. We derive the Cramer-Rao lower bound (CRLB) and utilize the Neyman-Fisher factorization method to obtain the sufficient statistics. Leveraging the Rao-Blackwell-Lehmann-Scheffe (RBLS) theorem, we derive the MVU estimator. Our results demonstrate a close alignment between the proposed estimator’s variance and the CRLB. Furthermore, we observe that increased user velocity leads to higher velocity estimation variance, indicating greater challenges in accurate estimation for faster-moving users. Simulation results show that a higher BS density and longer handover measurement periods can substantially reduce velocity estimation errors, highlighting the benefits of an improved HetNet infrastructure and extended measurement durations for precise velocity estimation.