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Network-Based UE Mobility Estimation in Mobile Networks

Dalia-Georgiana Herculea 1, 2, 3 Majed Haddad 4 Veronique Capdevielle 5 Chung Shue Chen 2, 3
1 DYOGENE - Dynamics of Geometric Networks
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique : UMR 8548, Inria de Paris
4 MAESTRO - Models for the performance analysis and the control of networks
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The coexistence of small cells and macro cells is a key feature of 4G and future networks. This heterogeneity, together with the increased mobility of user devices can generate a high handover frequency that could lead to unreasonably high call drop probability or poor user experience. By performing smart mobility management, the network can pro-actively adapt to the user and guarantee seamless and smooth cell transitions. In this work, we introduce an algorithm that takes as input sounding reference signal (SRS) measurements available at the base station (eNodeB in 4G systems) to estimate with a low computational requirement the mobility level of the user and with no modification at the user device/equipment (UE) side. The performance of the algorithm is showcased using realistic data and mobility traces. Results show that the classification of UE speed to three mobility classes can be achieved with accuracy of 87% for low mobility, 93% for medium mobility, and 94% for high mobility, respectively.
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Contributor : Dalia-Georgiana Popescu <>
Submitted on : Monday, December 12, 2016 - 10:51:39 AM
Last modification on : Monday, December 14, 2020 - 9:50:56 AM
Long-term archiving on: : Tuesday, March 28, 2017 - 12:08:22 AM


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Dalia-Georgiana Herculea, Majed Haddad, Veronique Capdevielle, Chung Shue Chen. Network-Based UE Mobility Estimation in Mobile Networks. S3 '15 Proceedings of the 2015 Workshop on Wireless of the Students, by the Students, & for the Students , Sep 2015, Paris, France. pp.1 - 3, ⟨10.1145/2801694.2801704⟩. ⟨hal-01414195⟩



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