Improved Markov predictor in wireless networks

Y. Yuan, L. Huang, Y. Tang, Der-Jiunn Deng, D. C. Huang

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

As wireless networks have been an integral part of our daily life, mobility prediction techniques have become one of the main topics in current research efforts. An accurate prediction of the next cell to which the mobile users are going can greatly improve the performance of wireless applications, such as map resource allocation, congestion control, quality of service and mobility management. It has been shown that the Markov predictor is a good mobility predictor in actual wireless local area network environments. However, from the standpoint of conditional entropy, the authors analyse that the Markov predictor has the disadvantage of performing worse when the location history is lacking or an approximate tie has happened. As a consequence, a novel improved Markov predictor is proposed, and simulations are conducted to evaluate the performance of the proposed scheme. The simulation results show that the improved Markov predictor solves not only the disadvantages of Markov predictor due to the lack of location history information, but also the expansion of state space in multiple-order Markov predictors.

Original languageEnglish
Pages (from-to)1823-1828
Number of pages6
JournalIET Communications
Volume5
Issue number13
DOIs
Publication statusPublished - 2011 Sep 5

Fingerprint

Wireless networks
Congestion control (communication)
Wireless local area networks (WLAN)
Resource allocation
Quality of service
Entropy

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Yuan, Y. ; Huang, L. ; Tang, Y. ; Deng, Der-Jiunn ; Huang, D. C. / Improved Markov predictor in wireless networks. In: IET Communications. 2011 ; Vol. 5, No. 13. pp. 1823-1828.
@article{13a68b489fbb418d9b57ebc3def1e002,
title = "Improved Markov predictor in wireless networks",
abstract = "As wireless networks have been an integral part of our daily life, mobility prediction techniques have become one of the main topics in current research efforts. An accurate prediction of the next cell to which the mobile users are going can greatly improve the performance of wireless applications, such as map resource allocation, congestion control, quality of service and mobility management. It has been shown that the Markov predictor is a good mobility predictor in actual wireless local area network environments. However, from the standpoint of conditional entropy, the authors analyse that the Markov predictor has the disadvantage of performing worse when the location history is lacking or an approximate tie has happened. As a consequence, a novel improved Markov predictor is proposed, and simulations are conducted to evaluate the performance of the proposed scheme. The simulation results show that the improved Markov predictor solves not only the disadvantages of Markov predictor due to the lack of location history information, but also the expansion of state space in multiple-order Markov predictors.",
author = "Y. Yuan and L. Huang and Y. Tang and Der-Jiunn Deng and Huang, {D. C.}",
year = "2011",
month = "9",
day = "5",
doi = "10.1049/iet-com.2010.0337",
language = "English",
volume = "5",
pages = "1823--1828",
journal = "IET Communications",
issn = "1751-8628",
publisher = "Institution of Engineering and Technology",
number = "13",

}

Yuan, Y, Huang, L, Tang, Y, Deng, D-J & Huang, DC 2011, 'Improved Markov predictor in wireless networks', IET Communications, vol. 5, no. 13, pp. 1823-1828. https://doi.org/10.1049/iet-com.2010.0337

Improved Markov predictor in wireless networks. / Yuan, Y.; Huang, L.; Tang, Y.; Deng, Der-Jiunn; Huang, D. C.

In: IET Communications, Vol. 5, No. 13, 05.09.2011, p. 1823-1828.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Improved Markov predictor in wireless networks

AU - Yuan, Y.

AU - Huang, L.

AU - Tang, Y.

AU - Deng, Der-Jiunn

AU - Huang, D. C.

PY - 2011/9/5

Y1 - 2011/9/5

N2 - As wireless networks have been an integral part of our daily life, mobility prediction techniques have become one of the main topics in current research efforts. An accurate prediction of the next cell to which the mobile users are going can greatly improve the performance of wireless applications, such as map resource allocation, congestion control, quality of service and mobility management. It has been shown that the Markov predictor is a good mobility predictor in actual wireless local area network environments. However, from the standpoint of conditional entropy, the authors analyse that the Markov predictor has the disadvantage of performing worse when the location history is lacking or an approximate tie has happened. As a consequence, a novel improved Markov predictor is proposed, and simulations are conducted to evaluate the performance of the proposed scheme. The simulation results show that the improved Markov predictor solves not only the disadvantages of Markov predictor due to the lack of location history information, but also the expansion of state space in multiple-order Markov predictors.

AB - As wireless networks have been an integral part of our daily life, mobility prediction techniques have become one of the main topics in current research efforts. An accurate prediction of the next cell to which the mobile users are going can greatly improve the performance of wireless applications, such as map resource allocation, congestion control, quality of service and mobility management. It has been shown that the Markov predictor is a good mobility predictor in actual wireless local area network environments. However, from the standpoint of conditional entropy, the authors analyse that the Markov predictor has the disadvantage of performing worse when the location history is lacking or an approximate tie has happened. As a consequence, a novel improved Markov predictor is proposed, and simulations are conducted to evaluate the performance of the proposed scheme. The simulation results show that the improved Markov predictor solves not only the disadvantages of Markov predictor due to the lack of location history information, but also the expansion of state space in multiple-order Markov predictors.

UR - http://www.scopus.com/inward/record.url?scp=80053264434&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053264434&partnerID=8YFLogxK

U2 - 10.1049/iet-com.2010.0337

DO - 10.1049/iet-com.2010.0337

M3 - Article

AN - SCOPUS:80053264434

VL - 5

SP - 1823

EP - 1828

JO - IET Communications

JF - IET Communications

SN - 1751-8628

IS - 13

ER -