标题:Deterministic process-based generative models for characterizing packet-level bursty error sequences
作者:He, Yejun;Salih, Omar S.;Wang, Cheng-Xiang;Yuan, Dongfeng
通讯作者:Wang, Cheng-Xiang
作者机构:[He, Y] College of Information Engineering, Shenzhen University, Shenzhen, 518060, China;[ Salih, O.S] Joint Research Institute for Signal and Image P 更多
来源:Wireless communications & mobile computing
出版年:2015
卷:15
期:3
页码:421-430
DOI:10.1002/wcm.2356
关键词:generative models;digital wireless channels;burst error statistics;deterministic fading processes;Markov models
摘要: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. Copyright (c) 2013 John Wiley & Sons, Ltd.
收录类别:EI;SCOPUS;SCIE
WOS核心被引频次:3
Scopus被引频次:8
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921323252&doi=10.1002%2fwcm.2356&partnerID=40&md5=372cc8238e17195483b604075a5f2889
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