MDG: measurement-driven guidelines for 802.11 WLAN design

  • Authors:
  • Ioannis Broustis;Konstantina Papagiannaki;Srikanth V. Krishnamurthy;Michalis Faloutsos;Vivek Mhatre

  • Affiliations:
  • University of California;Intel Research;University of California;University of California;Bell Labs

  • Venue:
  • Proceedings of the 13th annual ACM international conference on Mobile computing and networking
  • Year:
  • 2007

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Abstract

Dense deployments of WLANs suffer from increased interference and as a result, reduced capacity. There are three main functions used to improve the overall network capacity: a) intelligent frequency allocation across APs, b) load-balancing of user affiliations across APs, and c) adaptive power-control for each AP. Several algorithms have been proposed in each category, but so far, their evaluation has been limited to: (a) each approach in isolation and, (b)simulations or small-scale testbeds. In this paper, we ask the question: what is the best way to combine these different functions? Our focus is to fully explore the interdependencies between the three functions in order to understand when and how to deploy them on a network. We follow a measurement-driven study to quantify the effects of three previously proposed optimization schemes (one for each category) on a relatively large testbed and in many different scenarios. Surprisingly, we find that blindly applying all the three optimization schemes is not always preferable; it can sometimes degrade the performance by as much as 24% compared to using only two of the schemes. We discover that there are explicit conditions that are conducive for applying specific combinations of the optimization schemes. We capture those conditions within a comprehensive framework, which we call MDG (Measurement-Driven Guidelines). While we derive suchguidelines based on measurements on one experimental testbed, we test their applicability and efficacy on a second testbed in a different location. We show that our framework improves network capacity consistently acrossboth testbeds, with improvements ranging from 22% to 142% with 802.11a, and 103% to 274% with 802.11g.