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CLC number: TN911

On-line Access: 2013-01-03

Received: 2012-06-05

Revision Accepted: 2012-11-18

Crosschecked: 2012-12-11

Cited: 1

Clicked: 4282

Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2013 Vol.14 No.1 P.42-49

http://doi.org/10.1631/jzus.C1200175


An independent but not identically distributed bit error model for heavy-tailed wireless channels


Author(s):  Jia Lu, Wei Yang, Jun-hui Wang, Bao-liang Li, Wen-hua Dou

Affiliation(s):  School of Computer, National University of Defense Technology, Changsha 410073, China; more

Corresponding email(s):   lujia661@126.com

Key Words:  Trace, Heavy-tailed, Independent but not identically distributed (inid), Bit error model, Bursty


Jia Lu, Wei Yang, Jun-hui Wang, Bao-liang Li, Wen-hua Dou. An independent but not identically distributed bit error model for heavy-tailed wireless channels[J]. Journal of Zhejiang University Science C, 2013, 14(1): 42-49.

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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1200175


Abstract: 
The error patterns of a wireless channel can be represented by a binary sequence of ones (burst) and zeros (run), which is referred to as a trace. Recent surveys have shown that the run length distribution of a wireless channel is an intrinsically heavy-tailed distribution. Analytical models to characterize such features have to deal with the trade-off between complexity and accuracy. In this paper, we use an independent but not identically distributed (inid) stochastic process to characterize such channel behavior and show how to parameterize the inid bit error model on the basis of a trace. The proposed model has merely two parameters both having intuitive meanings and can be easily figured out from a trace. Compared with chaotic maps, the inid bit error model is simple for practical use but can still be deprived from heavy-tailed distribution in theory. Simulation results demonstrate that the inid model can match the trace, but with fewer parameters. We then propose an improvement on the inid model to capture the ‘bursty’ nature of channel errors, described by burst length distribution. Our theoretical analysis is supported by an experimental evaluation.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[9]Qureshi, H.K., Ahmad, J.J., Khayam, S.A., Rakocevic, V., Rajarajan, M., 2011. Graph-theoretic complexity reduction for Markovian wireless channel models. Wirel. Pers. Commun., 58(4):831-849.

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[12]Wang, C.X., Xu, W., 2007. A new class of generative models for burst error characterization in digital wireless channels. IEEE Trans. Commun., 55(3):453-462.

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