Estimation of Gilbert's and Fritchman's models parameters using the gradient method for digital mobile radio channels

Chouinard, Jean‐Yves; Lecours, M.; Delisle, G.Y.

doi:10.1109/25.16542

Cited by 50 publications

(22 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Further, cochannel interference was simulated at various carrier-to-interference ratios (CIR). In using the SMAP1 and SMAP2 schemes, the channel transition probabilities have to be combined with a priori knowledge of Gilbert model parameters which can be estimated once in advance using the gradient iterative method (Chouinard et al, 1988). For each simulated error sequence, we first measured the error-gap distribution P(0 l |1) by computing the probability that at least l successive error-free bits will be encountered next on the condition that an error bit has just occurred.…”

Section: Resultsmentioning

confidence: 99%

Channel‐optimized error mitigation for distributed speech recognition over wireless networks

Lee

Chang²

2009

Journal of the Chinese Institute of Engineers

View full text Add to dashboard Cite

This paper investigates the error mitigation algorithms for distributed speech recognition over wireless channels. A MAP symbol decoding algorithm which exploits the combined a priori information of source and channel is proposed. This is used in conjunction with a modified BCJR algorithm for decoding convolutional codes based on sectionalized code trellises. Performance is further enhanced by the use of the Gilbert channel model that more closely characterizes the statistical dependencies between channel bit errors. Experiments on Mandarin digit string recognition task indicate that our proposed mitigation scheme achieves high robustness against channel errors.

show abstract

Section: Resultsmentioning

confidence: 99%

Channel‐optimized error mitigation for distributed speech recognition over wireless networks

Lee

Chang²

2009

Journal of the Chinese Institute of Engineers

View full text Add to dashboard Cite

show abstract

“…Hence, if P DCCA (0), P DCCA (00), P DCCA (000), and P DCCA (111) are known, where P DCCA (z n ) is the probability of error sequences generated by the DCCA [see (11)], the parameters of the GEC can be obtained by (16), (17), and Proposition 1 by setting P GEC (z n ) = P DCCA (z n ), n = 1, 2, and 3.…”

Section: ) Qbc Parameter Estimationmentioning

confidence: 99%

A Model for Correlated Rician Fading Channels Based on a Finite Queue

Zhong¹,

Alajaji

Takahara

2008

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Abstract-We study the problem of approximating the family of hard-decision frequency-shift keying demodulated correlated flat Rician fading channels via a recently introduced queue-based channel (QBC) model for binary communication channels with memory. For a given "discretized" fading channel, we construct a QBC whose noise process is statistically "close" in the Kullback-Leibler sense to the error or noise process that is generated by the fading channel, and the modeling accuracy is evaluated in terms of noise autocorrelation function (ACF) and channel capacity. Numerical results indicate that the QBC provides a good approximation of the fading channels for a wide range of channel conditions. Furthermore, it estimates the noise ACF more accurately than the finite-state Markov models that have been recently studied by Pimentel et al., while, at the same time, remaining mathematically tractable.Index Terms-Autocorrelation function (ACF), capacity, correlated Rician fading, error statistics, Kullback-Leibler divergence rate (KLDR), modeling of communication channels with memory.

show abstract

“…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%

“…8), there are two main areas with high probability of gaplengths. Obviously, these statistics of gaplengths cannot be met via a single geometric distribution; therefore, the memoryless When using Fritchman's partitioned Markov chains with two error-free states and following the proposal of [15] (estimating the model parameters by separate curve fitting for different parts of the distribution function), the measured distribution of gaplengths is met as shown in Fig. 18.…”

Section: Umts Dch Link Error Modeling -Static Casementioning

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

View full text Add to dashboard Cite

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.

show abstract

Estimation of Gilbert's and Fritchman's models parameters using the gradient method for digital mobile radio channels

Cited by 50 publications

References 2 publications

Channel‐optimized error mitigation for distributed speech recognition over wireless networks

Channel‐optimized error mitigation for distributed speech recognition over wireless networks

A Model for Correlated Rician Fading Channels Based on a Finite Queue

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

Contact Info

Product

Resources

About