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Cited by 473

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Finite-state Markov modeling of fading channels - a survey of principles and applications

Sadeghi, P., Kennedy, R., Rapajic, P., Shams, R.·2008·IEEE Signal Processing Magazine

Abstract

This article's goal is to provide an in-depth understanding of the principles of FSMC modeling of fading channels with its applications in wireless communication systems. While the emphasis is on frequency nonselective or flat-fading channels, this understanding will be useful for future generalizations of FSMC models for frequency-selective fading channels. The target audience of this article include both theory- and practice-oriented researchers who would like to design accurate channel models for evaluating the performance of wireless communication systems in the physical or media access control layers, or those who would like to develop more efficient and reliable transceivers that take advantage of the inherent memory in fading channels. Both FSMC models and flat-fading channels will be formally introduced. FSMC models are particulary suitable to represent and estimate the relatively fast flat-fading channel gain in each subcarrier.

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Metadata

Authors
Sadeghi, P., Kennedy, R., Rapajic, P., & Shams, R.
Venue
IEEE Signal Processing Magazine
Published
2008-09
Volume
25
Issue
5
Pages
57-80
ISSN
1053-5888 (ISSN-L)
OpenAlex
W2119683335
Cited by
473
References
1
FWCI
14.94
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Finite-state Markov modeling of fading channels - a survey of principles and applications. https://app.cochat.ai/papers/10.1109/msp.2008.926683
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Sadeghi, P., Kennedy, R., Rapajic, P., & Shams, R. (2008). Finite-state Markov modeling of fading channels - a survey of principles and applications. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2008.926683

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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.