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Mobility prediction in 4G D-Cov Networks

Mazen Tlais 1 Frédéric Weis 1
1 ACES - Ambient computing and embedded systems
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The Infostations system has been introduced to recover the bottleneck of the cellular networks in terms of cost and bandwidth. In this system, small and separated islands of coverage provide intermittent but very high speed rate. The short time the mobile terminal spends in the coverage area has direct impacts on the network performance. The mobile terminal is involved to hide the discontinuous coverage by buffering data in its local cache. The mobile terminal is concerned to maximize the utilization of the bandwidth during the coverage time. To do so, it is essential to avoid the bandwidth wasting due to the overall delay, mainly, the delay occurred when the mobile terminal enters new point of attachment. The network must be able to identify the next point of attachment before the mobile terminal reaches it. This work analyses two well known solutions proposed to handle the mobility prediction in wireless networks. One sees the limitation of these protocols to enable the mobility prediction in the Infostations system. A new mobility prediction protocol is proposed to handle a neighbor graph table that dynamically configures itself according to the terminals' mobility. This protocol is named NDP, Neighbor Discovery Protocol. NDP takes into account the different constraints of the Infostations system particularly the discontinuous coverage. The simulation shows that the the terminals service continuity is significantly improved thanks to NDP.
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https://hal.inria.fr/inria-00070380
Contributor : Rapport de Recherche Inria <>
Submitted on : Friday, May 19, 2006 - 8:19:16 PM
Last modification on : Thursday, January 7, 2021 - 4:28:54 PM
Long-term archiving on: : Tuesday, February 22, 2011 - 11:42:55 AM

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  • HAL Id : inria-00070380, version 1

Citation

Mazen Tlais, Frédéric Weis. Mobility prediction in 4G D-Cov Networks. [Research Report] RR-5628, INRIA. 2005, pp.23. ⟨inria-00070380⟩

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