Modeling burst channels using partitioned Fritchman's Markov models

Pimentel, Cecílio; Blake, Ian F.

doi:10.1109/25.704842

Cited by 46 publications

(33 citation statements)

References 20 publications

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“…We define a binary error process , where indicates no symbol error at the th interval and indicates a symbol error. It can be shown that the probability of an error sequence of length , , may be expressed as [4] …”

Section: Channel Modelmentioning

confidence: 99%

“…The FSC model describes the successes and failures of the symbol transmitted over the fading channel, which is represented mathematically as a binary error sequence. The majority of models previously reported in the literature to characterize the symbol-by-symbol dynamics of the error sequence is based on a particular class of hidden Markov models called the simplified Fritchman model [4], [5]. Models based on a finite queue have been proposed recently [28].…”

Section: Introductionmentioning

confidence: 99%

“…As a consequence, abrupt changes in the mean received signal level may occur and the autocorrelation function (ACF) of the fading process may lead to the occurrence of a burst of bit errors. The analytical analysis of such a bursty communication system involves the calculation of the multivariate probability density function (pdf) of the correlated fading process [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Finite-State Markov Modeling of Correlated Rician-Fading Channels

Pimentel

Falk

Lisboa

2004

IEEE Trans. Veh. Technol.

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107

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Abstract-Stochastic properties of the binary channel that describe the successes and failures of the transmission of a modulated signal over a time-correlated flat-fading channel are considered for investigation. This analysis is employed to develop th-order Markov models for such a burst channel. The order of the Markov model that generates accurate analytical models is estimated for a broad range of fading environments. The parameterization and accuracy of an important class of hidden Markov models, known as the Gilbert-Elliott channel (GEC), are also investigated. Fading rates are identified in which the th-order Markov model and the GEC model approximate the fading channel with similar accuracy. The latter model is useful for approximating slowly fading processes, since it provides a more compact parameterization.

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Section: Channel Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Finite-State Markov Modeling of Correlated Rician-Fading Channels

Pimentel

Falk

Lisboa

2004

IEEE Trans. Veh. Technol.

Self Cite

107

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“…In [15] and [16], methods for the parameter estimation of Gilbert's and Fritchman's models are presented, while in [17], [18], [19] and [20] HMMs in general are addressed. New models to describe special error characteristics of (wireless) communication links have been developed up to the present, including a three-state model for Poisson distributed burstlengths [21], a two-state model with segmented exponential distributed burst-and gaplengths [22], two-and three-state Markov models [23] and a finite state Markov channel (FSMC) [24].…”

Section: A Link Error Models -A Literature Surveymentioning

confidence: 99%

Link Error Analysis and Modeling for Video Streaming Cross-Layer Design in Mobile Communication Networks

Karner¹,

Nemethova²,

Svoboda³

et al. 2007

ETRI J

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Particularly in wireless communications, link errors severely affect the quality of the services due to the high error probability and the specific error characteristics (burst errors) in the radio access part of the network. In this work, we show that thorough analysis and appropriate modeling of radio-link error behavior are essential to evaluate and optimize higher layer protocols and services. They are also the basis for finding network-aware crosslayer processing algorithms which are capable of exploiting the specific properties of the link error statistics, such as predictability. This document presents the analysis of the radio link errors based on measurements in live Universal Mobile Telecommunication System (UMTS) radio access networks as well as new link error models originating from that analysis. It is shown that the knowledge of the specific link error characteristics leads to significant improvements in the quality of streamed video by applying the proposed novel network-and content-aware cross-layer scheduling algorithms. Although based on live UMTS network experience, many of the conclusions in this work are of general validity and are not limited to UMTS only.

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“…Progress has been achieved on the statistical and information theoretic modeling of channels with memory (e.g., see [1], [6], [10], [11]), as well as on the design of effective iterative decoders for such channels (e.g., see [2], [3], [7], [8]). However, little is known about the structure of optimal maximum likelihood (ML) decoders for such channels.…”

Section: Introductionmentioning

confidence: 99%

On Decoding Binary Quasi-Perfect Codes Over Markov Noise Channels

Al-Lawati¹,

Alajaji²

2007

2007 10th Canadian Workshop on Information Theory (CWIT)

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Abstract-We study the decoding problem when a binary linear quasi-perfect code is transmitted over a binary channel with additive Markov noise. After examining the properties of the channel block transition distribution, we show a near equivalence relationship between strict maximum likelihood and strict minimum Hamming distance decoding for a range of channel parameters and the code's minimum distance. As a result, an improved minimum distance decoder is proposed and simulations illustrating its benefits are provided.

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Modeling burst channels using partitioned Fritchman's Markov models

Cited by 46 publications

References 20 publications

Finite-State Markov Modeling of Correlated Rician-Fading Channels

Finite-State Markov Modeling of Correlated Rician-Fading Channels

Link Error Analysis and Modeling for Video Streaming Cross-Layer Design in Mobile Communication Networks

On Decoding Binary Quasi-Perfect Codes Over Markov Noise Channels

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