Throughput-based rate adaptation algorithm for IEEE 802.11 vehicle networks

Ayoade Ilori, Zuoyin Tang, Jianhua He*, Yue Li

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

A key problem with IEEE 802.11 technology is adaptation of the transmission rates to the changing channel conditions, which is more challenging in vehicular networks. Although rate adaptation problem has been extensively studied for static residential and enterprise network scenarios, there is little work dedicated to the IEEE 802.11 rate adaptation in vehicular networks. Here, the authors are motivated to study the IEEE 802.11 rate adaptation problem in infrastructure-based vehicular networks. First of all, the performances of several existing rate adaptation algorithms under vehicle network scenarios, which have been widely used for static network scenarios, are evaluated. Then, a new rate adaptation algorithm is proposed to improve the network performance. In the new rate adaptation algorithm, the technique of sampling candidate transmission modes is used, and the effective throughput associated with a transmission mode is the metric used to choose among the possible transmission modes. The proposed algorithm is compared to several existing rate adaptation algorithms by simulations, which shows significant performance improvement under various system and channel configurations. An ideal signal-to-noise ratio (SNR)-based rate adaptation algorithm in which accurate channel SNR is assumed to be always available is also implemented for benchmark performance comparison.
Original languageEnglish
Pages (from-to)111–118
Number of pages8
JournalIET Networks
Volume4
Issue number2
Early online date4 Aug 2014
DOIs
Publication statusPublished - Mar 2015

Bibliographical note

This paper is a postprint of a paper submitted to and accepted for publication in IET networks and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.

Funding: UK EPSRC (grant reference number EP/1010157/1) and the National Natural Science Foundation of China (NSFC) grant number 61103177

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