Wireless Transmission of Big Data: A Transmission Time Analysis over Fading Channel

Handle URI:
http://hdl.handle.net/10754/627615
Title:
Wireless Transmission of Big Data: A Transmission Time Analysis over Fading Channel
Authors:
Wang, Wen-Jing; Yang, Hong-Chuan; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 )
Abstract:
In this paper, we investigate the transmission time of a large amount of data over fading wireless channel with adaptive modulation and coding (AMC). Unlike traditional transmission systems, where the transmission time of a fixed amount of data is typically regarded as a constant, the transmission time with AMC becomes a random variable, as the transmission rate varies with the fading channel condition. To facilitate the design and optimization of wireless transmission schemes for big data applications, we present an analytical framework to determine statistical characterizations for the transmission time of big data with AMC. In particular, we derive the exact statistics of transmission time over block fading channels. The probability mass function (PMF) and cumulative distribution function (CDF) of transmission time are obtained for both slow and fast fading scenarios. We further extend our analysis to Markov channel, where transmission time becomes the sum of a sequence of exponentially distributed time slots. Analytical expression for the probability density function (PDF) of transmission time is derived for both fast fading and slow fading scenarios. These analytical results are essential to the optimal design and performance analysis of future wireless transmission systems for big data applications.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program
Citation:
Wang W-J, Yang H-C, Alouini M-S (2018) Wireless Transmission of Big Data: A Transmission Time Analysis over Fading Channel. IEEE Transactions on Wireless Communications: 1–1. Available: http://dx.doi.org/10.1109/TWC.2018.2822801.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Wireless Communications
Issue Date:
10-Apr-2018
DOI:
10.1109/TWC.2018.2822801
Type:
Article
ISSN:
1536-1276
Additional Links:
https://ieeexplore.ieee.org/document/8334702/
Appears in Collections:
Articles; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, Wen-Jingen
dc.contributor.authorYang, Hong-Chuanen
dc.contributor.authorAlouini, Mohamed-Slimen
dc.date.accessioned2018-04-24T06:46:19Z-
dc.date.available2018-04-24T06:46:19Z-
dc.date.issued2018-04-10en
dc.identifier.citationWang W-J, Yang H-C, Alouini M-S (2018) Wireless Transmission of Big Data: A Transmission Time Analysis over Fading Channel. IEEE Transactions on Wireless Communications: 1–1. Available: http://dx.doi.org/10.1109/TWC.2018.2822801.en
dc.identifier.issn1536-1276en
dc.identifier.doi10.1109/TWC.2018.2822801en
dc.identifier.urihttp://hdl.handle.net/10754/627615-
dc.description.abstractIn this paper, we investigate the transmission time of a large amount of data over fading wireless channel with adaptive modulation and coding (AMC). Unlike traditional transmission systems, where the transmission time of a fixed amount of data is typically regarded as a constant, the transmission time with AMC becomes a random variable, as the transmission rate varies with the fading channel condition. To facilitate the design and optimization of wireless transmission schemes for big data applications, we present an analytical framework to determine statistical characterizations for the transmission time of big data with AMC. In particular, we derive the exact statistics of transmission time over block fading channels. The probability mass function (PMF) and cumulative distribution function (CDF) of transmission time are obtained for both slow and fast fading scenarios. We further extend our analysis to Markov channel, where transmission time becomes the sum of a sequence of exponentially distributed time slots. Analytical expression for the probability density function (PDF) of transmission time is derived for both fast fading and slow fading scenarios. These analytical results are essential to the optimal design and performance analysis of future wireless transmission systems for big data applications.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttps://ieeexplore.ieee.org/document/8334702/en
dc.rights(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectadaptive modulation and codingen
dc.subjectBig Dataen
dc.subjectblock fadingen
dc.subjectCoherenceen
dc.subjectdistribution functionsen
dc.subjectEncodingen
dc.subjectFading channelsen
dc.subjectMarkov channelen
dc.subjectModulationen
dc.subjectSignal to noise ratioen
dc.subjectTransmission timeen
dc.subjectWireless communicationen
dc.titleWireless Transmission of Big Data: A Transmission Time Analysis over Fading Channelen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.identifier.journalIEEE Transactions on Wireless Communicationsen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada.en
kaust.authorAlouini, Mohamed-Slimen
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