Deterministic process-based generative models for characterizing packet-level bursty error sequences

Yejun He, Omar S. Salih, Cheng Xiang Wang, Dongfeng Yuan

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Errors encountered in digital wireless channels are not independent but rather form bursts or clusters. Error models aim to investigate the statistical properties of bursty error sequences at either packet level or bit level. Packet-level error models are crucial to the design and performance evaluation of high-layer wireless communication protocols. This paper proposes a general design procedure for a packet-level generative model based on a sampled deterministic process with a threshold detector and two parallel mappers. In order to assess the proposed method, target packet error sequences are derived by computer simulations of a coded enhanced general packet radio service system. The target error sequences are compared with the generated error sequences from the deterministic process-based generative model using some widely used burst error statistics, such as error-free run distribution, error-free burst distribution, error burst distribution, error cluster distribution, gap distribution, block error probability distribution, block burst probability distribution, packet error correlation function, normalized covariance function, gap correlation function, and multigap distribution. The deterministic process-based generative model is observed to outperform the widely used Markov models.

Original languageEnglish
Pages (from-to)421-430
Number of pages10
JournalWireless Communications and Mobile Computing
Volume15
Issue number3
Early online date7 Feb 2013
DOIs
Publication statusPublished - 25 Feb 2015

Keywords

  • Burst error statistics
  • Deterministic fading processes
  • Digital wireless channels
  • Generative models
  • Markov models

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Information Systems

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