Measurement-driven guidelines for 802.11 WLAN design

  • Authors:

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

  • Affiliations:

  • Department of Computer Science and Engineering, University of California Riverside, Riverside, CA;Intel Labs, Pittsburgh, PA;Department of Computer Science and Engineering, University of California Riverside, Riverside, CA;Department of Computer Science and Engineering, University of California Riverside, Riverside, CA;Motorola Inc., Arlington Heights, IL

  • Venue:
  • IEEE/ACM Transactions on Networking (TON)
  • Year:
  • 2010

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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: 1) intelligent frequency allocation across access points (APs); 2) load-balancing of user affiliations across APs; and 3) 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 these conditions within a comprehensive framework, which we call measurement-driven guidelines (MDG). While we derive such guidelines 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 across both testbeds, with improvements ranging from 22% to 142% with 802.11a, and 103% to 274% with 802.11g.