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

Abstract

In wireless cellular networks with densely deployed base stations, knowing the velocities of mobile devices is a key to avoid call drops and improve the quality of service to the user equipments (UEs). 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 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 then analyze the statistics of the number of handovers as a function of UE velocity, small-cell density, and handover count measurement time window. Using these statistics, we derive approximations to the Cramer-Rao lower bound (CRLB) for the velocity estimate of a UE. Also, we determine a minimum variance unbiased (MVU) velocity estimator whose variance tightly matches with the CRLB. Using this velocity estimator, we formulate the problem of detecting the mobility state of a UE as low, medium, or high-mobility, as in LTE specifications. Subsequently, we derive the probability of correctly detecting the mobility state of a UE. Finally, we evaluate the accuracy of the velocity estimator under more realistic scenarios such as clustered deployment of small cells, random way point (RWP) mobility model for UEs, and variable UE velocity. Our analysis shows that the accuracy of velocity estimation and mobility state detection increases with increasing small cell density and with increasing handover count measurement time window.keywords: Cramer-Rao lower bound (CRLB), heterogeneous networks (HetNets), long term evolution (LTE), mobility state estimation, mobile velocity estimation, phantom cell, small cells.

A Survey of Enabling Technologies for Network Localization, Tracking, and Navigation

“…A HO zone is the most probable location in a given road segment where the mobile switches from the current BS to a new one. Trajectory-based weighted MSE that counts HO medium (60 km/h), 84%, 45%, 23% (macro-only) information successes and RSRP threshold crossing events high (120 km/h) 85%, 47%, 25% (2 picocells/macro) Enhanced Trajectory-based weighted MSE that 89%, 51%, 55% (2 picocells/macro) considers both successful and failed HO events [176] Stochastic geometry approach combined with 0-40 km/h, 40-80 km/h, RMSE 17 km/h at speed 60 km/h minimum variance unbiased speed estimator >80 km/h 98%, 67%, 85% (200 cells/km 2 )…”

“…Another solution models densely deployed small cells using stochastic geometry, then analyzes the statistics of the number of HOs as a function of user device velocity, small-cell density, and HO count measurement time window, and develops a minimum variance unbiased velocity estimator, whose variance tightly matches with the Cramer-Rao Lower Bound [176]. Using this velocity estimator, they formulate the problem of detecting the user mobility state as low, medium, or high.…”

“…using GPS location [166], signal power measurements [167]- [171] and HO information [172]- [176] in cellular networks.…”

Stochastic Geometry Based Analysis of Capacity, Mobility and Energy Efficiency for Dense Heterogeneous Networks

“…Additionally, the handover-count based velocity estimate will be used to detect the mobility state (low/medium/high) of a UE, and the expressions for the probability of detection and probability of false alarm will be derived. The CRLB of the sojourn time based method will be shown to be smaller than the CRLB of handover-count method [23,24,27].…”

“…The research to be presented in this dissertation resulted in several publications in IEEE conferences and journals [22][23][24][25][26][27][28][29][30][31][32][33]. The key contributions of this dissertation can be summarized as follows.…”