Mobility state estimation in LTE

Abstract

Estimating mobile user speed is a problematic issue which has significant impacts to radio resource management and also to the mobility management of Long Term Evolution (LTE) networks. This paper introduces two algorithms that can estimate the speed of mobile user equipments (UE), with low computational requirement, and without modification of neither current user equipment nor 3GPP standard protocol. The proposed methods rely on uplink (UL) sounding reference signal (SRS) power measurements performed at the eNodeB (eNB) and remain efficient with large sampling period (e.g., 40 ms or beyond). We evaluate the effectiveness of our algorithms using realistic LTE system data provided by the eNB Layer1 team of Alcatel-Lucent. Results show that the classification of UE's speed required by LTE can be achieved with high accuracy. In addition, they have minimal impact to the central processing unit (CPU) and the memory of eNB modem. We see that they are very practical to today's LTE networks and would allow a continuous and real-time UE speed estimation.

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

“…The MSE solution in [170] takes advantage of the speed dependent time variations of the shadowing in the uplink SRS measurements and the computed metrics are then compared with a reference curve or look-up table (database), with respect to the shadowing decorrelation distance. Two algorithms are presented, namely the Spectral Analysis Method (SAM), which evaluates the maximum frequency of oscillation of SRS measurements built on Fast Fourier Transform (FFT), and the Time-based Spectrum Spreading Method (TSSM), which evaluates in time domain the speed dependent spectrum spreading of the SRS signal.…”

Online mobile user speed estimation: Performance and tradeoff considerations

“…In [12], the second derivative of the autocorrelation function of the received signal amplitude r(t) is computed as:…”

“…In [12], we addressed the problem of determining UE speed and mobility class for mobility management in LTE radio access network (RAN). We proposed two methods for UE speed estimation: a spectral analysis method (SAM) and a time-based spectrum spreading method (TSSM).…”

Semi-Supervised Deep Learning-Based Methods for Indoor Outdoor Detection

“…Many works in literature have addressed the user mobility estimation. In [14], authors use RSRP measurement according to the speed dependent time variations of shadowing to compute the UE speed. They propose two methods: either based on a spectral analysis or based on a time-based spectrum spreading.…”