Achieving weighted fairness in WLAN mesh networks: An analytical model

Lei Lei*, Ting Zhang, Xiaoqin Song, Shengsuo Cai, Xiaoming Chen, Jinhua Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Multi-hop WLAN mesh networks employing the enhanced distributed channel access (EDCA) scheme as the medium access control protocol have been shown to suffer from serious throughput unfairness among competing flows. Indeed, some flows can even capture the whole channel bandwidth while other flows get starved. In this paper, we focus on achieving weighted fairness for differential services in WLAN mesh networks. We first introduce the concepts of the instantaneous collision zone and the persistent collision zone of receivers in multi-hop networks. Then we suggest that collisions induced by the hidden jammers located in the persistent collision zone of receivers are the primary causes for the throughput unfairness. We further develop a three-dimensional Markov chain model to determine how to precisely tune the backoff persistence factors to achieve the weighted fairness for flows with diverse quality of service (QoS) demands. In this model, we put forward the pseudo states to distinguish the different backoff procedures induced by the RTS collisions and the data collisions. Through analytical modeling, we get the proper backoff persistence factors to achieve a predefined weighted fairness goal. Finally, we validate the accuracy of our model by comparing the analytical results with that obtained by means of simulations.

Original languageEnglish
Pages (from-to)117-129
Number of pages13
JournalAd Hoc Networks
Volume25
Issue numberPA
DOIs
Publication statusPublished - 1 Feb 2015
Externally publishedYes

Keywords

  • Backoff persistence factors
  • Markov chain
  • Weighted fairness
  • Wireless mesh networks

Fingerprint

Dive into the research topics of 'Achieving weighted fairness in WLAN mesh networks: An analytical model'. Together they form a unique fingerprint.

Cite this