Wavelet analysis for velocity characterization in mobile networks

Elayoubi, Salah Eddine; Alaya, A.B.H.; Villebrun, E.; Fourestie, B.

doi:10.1109/vetecs.2006.1682933

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…In previous studies, we described some methods to better exploit existing radio measurements [2] [3]. The obtained results were very encouraging: using statistical tools, we extracted useful information of the state of the mobile (indoor, outdoor, incar, pedestrian, unmoving).…”

Section: Study Approachmentioning

confidence: 91%

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Optimization of Radio Measurements Exploitation in Wireless Mobile Networks

Alaya-Feki¹,

Cornec²,

Moulines

2007

JCM

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Radio measurements play a crucial role in mobile wireless networks as they are input of Radio Resource Management (RRM) mechanisms, needed to assess mobile network reliability and to supervise the planned coverage area. Nevertheless, these measurements are used in their crude state without any additional intelligent treatment whereas they are a rich and unexploited source of information for the network. In this paper, we propose a new approach for the manipulation of existing radio measurements in wireless mobile networks. The goal is to optimize the use of these measurements with new approaches of exploitation in different locations of the network, basing on advanced signal processing tools. This new approach is based on nonparametric regression and smoothing methods. Interesting results are inferred from this analysis, based on real measurement traces collected in different environments and situations. Main results are the elaboration of a dynamic estimator of the attenuations components (pathloss, shadowing and fast fading), a method for the determination of the mobile user situation (incar, pedestrian and unmoving) as well as ameliorations in the handover procedure. We conclude that integrating advanced radio signal processing in RRM mechanisms is an efficient and easy-toimplement way for a better use of radio measurements.

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Section: Study Approachmentioning

confidence: 91%

“…the wavelet transform with the multiresolution representation [2] and the likelihood ratio function associated with the Neyman-Pearson test [3].…”

Section: Study Approachmentioning

confidence: 99%