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Traffic-aware ACB scheme for massive access in machine-to-machine networks

  • Xi'an Jiaotong University
  • West Virginia University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

76 Scopus citations

Abstract

Supporting massive access of machine-type devices in a short period is a critical challenge in machine-to-machine (M2M) communications. We in this paper propose a traffic-aware Access Class Barring (ACB) scheme to improve the scalability of M2M networks. Unlike traditional ACB scheme, our proposed scheme aim at dynamically regulating the parameter of access probability, called barring factor, based on network load, thus accommodating much more M2M devices as well as lowering the access delay. To achieve this goal, we first develop a Markov-Chain based traffic-load estimation scheme according to the collision status. Then, we propose a spectrum of functions to control the barring factor varying with the estimated traffic load. Also provided is a set of simulations results, demonstrating that our proposed traffic-aware scheme significantly outperforms the traditional ACB scheme in terms of not only access success probability, but also average access delay.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Communications, ICC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages617-622
Number of pages6
ISBN (Electronic)9781467364324
DOIs
StatePublished - 9 Sep 2015
EventIEEE International Conference on Communications, ICC 2015 - London, United Kingdom
Duration: 8 Jun 201512 Jun 2015

Publication series

NameIEEE International Conference on Communications
Volume2015-September
ISSN (Print)1550-3607

Conference

ConferenceIEEE International Conference on Communications, ICC 2015
Country/TerritoryUnited Kingdom
CityLondon
Period8/06/1512/06/15

Keywords

  • IoT
  • LTE
  • M2M communications
  • Markov-Chain
  • barring factor
  • random access
  • traffic-aware ACB

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