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The ultra-wide bandwidth indoor channel: from statistical model to simulations

Cassioli, D., Win, M.Z., Molisch, A.F.·2002·IEEE Journal on Selected Areas in Communications

Abstract

We establish a statistical model for the ultra-wide bandwidth (UWB) indoor channel based on an extensive measurement campaign in a typical modern office building with 2-ns delay resolution. The approach is based on the investigation of the statistical properties of the multipath profiles measured in different rooms over a finely spaced measurement grid. The analysis leads to the formulation of a stochastic tapped-delay-line (STDL) model of the UWB indoor channel. The averaged power delay profile can be well-modeled by a single exponential decay with a statistically distributed decay constant. The small-scale statistics of path energy gains follow Gamma distributions whose parameters m are truncated Gaussian variables with mean values and standard deviations decreasing with delay. The total received energy experiences a lognormal shadowing around the mean energy given by the path-loss power law. We also find that the correlation between multipath components is negligible. Finally, we propose an implementation of the STDL model and give a comparison between the experimental data and the simulation results.

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Metadata

Authors
Cassioli, D., Win, M. Z., & Molisch, A. F.
Venue
IEEE Journal on Selected Areas in Communications
Published
2002-08
Volume
20
Issue
6
Pages
1247-1257
ISSN
0733-8716 (ISSN-L)
OpenAlex
W2134829276
Cited by
953
References
0
FWCI
70.10
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The ultra-wide bandwidth indoor channel: from statistical model to simulations. https://app.cochat.ai/papers/10.1109/jsac.2002.801228
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Cassioli, D., Win, M. Z., & Molisch, A. F. (2002). The ultra-wide bandwidth indoor channel: from statistical model to simulations. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/jsac.2002.801228

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.