Throughput‐based rate adaptation algorithm for IEEE 802.11 vehicle networks. Issue 2 (1st March 2015)

• Record Type: Journal Article
• Authors: Ilori, Ayoade; Tang, Zuoyin; He, Jianhua; Li, Yue
• Is Part Of: IET networks. Volume 4:Issue 2(2015)
• Page Start: 111
• Page End: 118
• Publication Date: 2015-03-01
• Subjects: wireless LAN -- wireless channels -- vehicular ad hoc networks; throughput‐based rate adaptation algorithm -- IEEE 802.11 vehicular network -- channel condition -- enterprise network scenario -- static residential scenario -- infrastructure‐based vehicular network -- sampling candidate transmission mode -- signal‐to‐noise ratio -- SNR; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2014.0043
• Languages: English
• ISSNs: 2047-4954

Abstract

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.