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Wireless Communications and Mobile Computing
Article . 2004 . Peer-reviewed
License: Wiley Online Library User Agreement
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Article . 2020
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Discrete Rayleigh fading channel modeling

Authors: Julio Aráuz; Prashant Krishnamurthy; Miguel A. Labrador;

Discrete Rayleigh fading channel modeling

Abstract

AbstractIn order to understand the behaviour of upper‐layer protocols and to design or fine tune their parameters over wireless networks, it is common to assume that the underlying channel is a flat Rayleigh fading channel. Such channels are commonly modeled as finite state Markov chains. Recently, hidden Markov models have also been employed to characterize these channels. In this paper, we study the different models that have been proposed along with the analysis of their validity. We start by presenting some preliminary concepts related to the modeling of the wireless communications channel. We then proceed to introduce finite state Markov channel models (FSMCs) along with the relations between them and the modulation schemes, error control protocols and channel coding. We propose and study the effects of taking into account the fading process in its characterization. We finish with a discussion on hidden Markov models for Rayleigh fading channel modeling. Copyright © 2004 John Wiley & Sons, Ltd.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Top 10%
Average
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