Straight: stochastic geometry and user history based mobility estimation

Abstract : 5G is envisioned to support scalable networks and improved user experience with virtually zero latency and ultra broad-band service. Supporting unlimited seamless mobility is one of the key issues and also for network resource utilization efficiency. In this paper, we focus on mobility management and user equipment (UE) speed class estimation, also known as mobility state estimation (MSE). We propose a method for estimating the UE mobility which is compliant with UE history information specifications by 3GPP (3rd Generation Partnership Project). We also exploit the impact of the environment on the UE trajectory and speed when determining UE mobility state. We evaluate the effectiveness of our algorithm using realistic mobility traces and network topology of the city of Cologne in Germany provided by the Kolntrace project [1]. Results show that the speed classification of UEs can be achieved with much higher accuracy compared to existing legacy 3GPP LTE MSE procedures.
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Contributor : Dalia-Georgiana Popescu <>
Submitted on : Monday, December 12, 2016 - 12:15:34 PM
Last modification on : Thursday, June 13, 2019 - 11:37:16 AM

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Dalia-Georgiana Herculea, Chung Shue Chen, Majed Haddad, Véronique Capdevielle. Straight: stochastic geometry and user history based mobility estimation. HotPOST '16 Proceedings of the 8th ACM International Workshop on Hot Topics in Planet-scale mObile computing and online Social neTworking, Jul 2016, Paderborn, Germany. pp.6, ⟨10.1145/2944789.2944790⟩. ⟨hal-01414185⟩

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