Abstract

Rate adaptation, which dynamically chooses transmission rate provided at the physical layer according to the current channel conditions, is a fundamental resource management issue in IEEE 802.11 networks with the goal of maximizing the network throughput. Traditional rate adaptation algorithms for IEEE 802.11n networks do not consider the interference problem, which becomes much more serious due to the rapid deployment of IEEE 802.11n devices and large number of mobile terminals. In this paper, an interference-aware rate and channel adaptation scheme RaCA for intensive IEEE 802.11n networks was proposed. Firstly, RaCA leverages RSSI and CSI information together to measure the current channel conditions at the receiver side. RSSI is a coarse-grained indicator and CSI is a fine-grained indicator. Secondly, a two-stage rate adaptation scheme TSRA was designed, which can quickly adapt to optimal bit rate based on RSSI and CSI information. Finally, a quorum-based channel adaptation algorithm QCA was proposed, which does not need control channel. If channel suffers severe interferences, RaCA calls QCA to choose another channel to work on. Simulation and testbed implementation results demonstrate that RaCA achieves significant throughput gain over SampleLite and Minstrel-HT.

Highlights

  • IEEE 802.11n is the most widely deployed wireless local area network (WLAN)

  • As shown in [3], the rate adaptation algorithms used in IEEE 802.11a/b/g networks turn out to be inefficient when applied in the IEEE 802.11n networks due to different physical and MAC layer technologies

  • We propose a joint rate and channel adaptation scheme RaCA based on the RSSI and CSI information, which only lowers the transmission rate due to channel-errors, and will select another channel to work on if the link suffers severe interferences

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Summary

Introduction

IEEE 802.11n is the most widely deployed wireless local area network (WLAN) It combines the new physical and link layer enhancements, including channel bonding, MIMO (Multiple-Input Multiple-Output), and frame aggregation [1]. As shown in [3], the rate adaptation algorithms used in IEEE 802.11a/b/g networks turn out to be inefficient when applied in the IEEE 802.11n networks due to different physical and MAC layer technologies. These observations led to new searches on rate adaptation schemes for IEEE 802.11n networks in the last few years.

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