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  1. Proceedings of the 8th ACM International Workshop on Hot Topics in Planet-scale mObile computing and online Social neTworking (HotPOST '16)
  2. Straight: stochastic geometry and user history based mobility estimation
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Straight: stochastic geometry and user history based mobility estimation
Enhancing opportunistic networking using location based social networks
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WI-FAB: attribute-based WLAN access control, without pre-shared keys and backend infrastructures
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Straight: stochastic geometry and user history based mobility estimation

Content Provider ACM Digital Library
Author Chen, Chung Shue Herculea, Dalia Capdevielle, Véronique Haddad, Majed
Abstract 5G is envisioned to support scalable networks and improved user experience with virtually zero latency and ultra broadband 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.
Starting Page 1
Ending Page 6
Page Count 6
File Format PDF
ISBN 9781450343442
DOI 10.1145/2944789.2944790
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-07-05
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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