Application of extreme value distribution to model propagation fading in indoor mobile radio environments

Molina-García, Mariano; Fernandez-Duran, A.; Alonso, J.I.

doi:10.1109/rws.2008.4463437

Cited by 7 publications

(2 citation statements)

References 7 publications

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“…Extreme Value Theory (EVT) provides a powerful and robust framework for the analysis of ultra-reliable communication by characterizing the extreme events that form the lower tail of a distribution [3]. EVT has been used in the upper layers to model tail statistics of queue length and delay [8]- [12], or in wireless channel modeling to provide a fit to the whole distribution of large scale or small scale fading [13]- [17]. [8] and [9] propose methods to reduce the number of vehicular users with large queue lengths by applying EVT on the extreme queue lengths.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, these studies use the existing average statistics-based channel models, which corresponds to the mode extrapolation in the ultra-reliable region. [13], [14], and [16] fit extreme value distribution (EVD) to the whole path-loss or power distribution and then claim that EVD can be used to model fading in wireless channel better than the well-known models. Additionally, in [15] and [17], generalized extreme value (GEV) distribution is used to model the small scale fading at maritime communication, and root-mean-square (RMS) delay spread at V2V communication, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications

Mehrnia¹,

Coleri²

2020

Preprint

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A key building block in the design of ultra-reliable communication systems is a wireless channel model that captures the statistics of rare events and significant fading dips. Ambitious reliability objectives, on the order of 10 5 − 10 9 packet error rate, only make sense when they are related to a statistical model of the environment in which the system is deployed. In this study, we propose a novel methodology based on extreme value theory (EVT) to statistically model the behavior of extreme events in a wireless channel for ultra-reliable communication. This methodology includes techniques for fitting the lower tail of the distribution to the generalized Pareto distribution, determination of optimum threshold over which the tail statistics are derived, derivation of optimum stopping condition on the sufficient number of samples required to apply EVT, and finally an assessment of the validity of the derived model. The analysis demonstrates that the proposed algorithm decreases the number of required samples for modeling the tail statistics by about 7 × 10 5 , and provides the best fit to the collected data and performs much better than the conventional methods based on the extrapolation of the average statistics in the ultra-reliable regime.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications

Mehrnia¹,

Coleri²

2020

Preprint

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Estimating changepoints in extremal dependence, applied to aviation stock prices during COVID-19 pandemic

Hazra,

Bose

2024

Journal of Applied Statistics

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Empirical estimation of probability distribution for electric field strength in automotive cabin

Maeda

Fukui

Ishihara

et al. 2011

2011 IEEE International Symposium on Electromagnetic Compatibility

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A novel model of the probability distribution for electric field strength in automotive cabin is proposed in the form of a mixture distribution of a linear Rayleigh distribution for low-value data and a log generalized extreme value (GEV) distribution for high-value data. A sample model is estimated from the data measured in a horizontal two-dimensional area above main battery in a hybrid vehicle. For the probability distribution model of the ensemble data of different transmitter antenna directions and all target frequencies, the mixture weights and the parameters for each component distribution are estimated using expectation-maximization (EM) algorithm. The result model showed good fitting to the measured data in probability density function plot and quantile-quantile (Q-Q) plot. Based on the ensemble data distribution model, the probability distribution models for individual conditions of frequencies and antenna-directions are also estimated applying linear transformation. Results are tested using Q-Q plot, which shows good fitting with slight overestimation in very high-value region. Kolmogorov-Smirnov (K-S) goodness of fit test at the 1% significance level also accepts this method for all the data measured above 600MHz.

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Application of extreme value distribution to model propagation fading in indoor mobile radio environments

Cited by 7 publications

References 7 publications

Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications

Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications

Estimating changepoints in extremal dependence, applied to aviation stock prices during COVID-19 pandemic

Empirical estimation of probability distribution for electric field strength in automotive cabin

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