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Modeling CSMA/CA network under asymmetric conditions

Published: 03 November 2013 Publication History

Abstract

Prior work reveals that unfairness in CSMA networks is mainly caused by three asymmetries: (i) information asymmetry; (ii) channel asymmetry; and (iii) rate asymmetry. In this paper, we develop a simple yet accurate analytical model that jointly characterizes all three asymmetric conditions. Our model allows us to estimate the throughput of two contending flows, given the contention window (CW) values. The model can be applied to remedy unfair issues in these topologies, by enabling CW tuning mechanisms to quantify the resulting throughput. The model is flexible enough to support three different fairness objectives. Further, we illustrate how to apply our CW tuning mechanism to an extensive set of asymmetric topologies. Using simulations and testbed measurements, we show that our CW tuning mechanism is effective in mitigating unfairness in the presence of the three fundamental asymmetric conditions.

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cover image ACM Conferences
PM2HW2N '13: Proceedings of the 8th ACM workshop on Performance monitoring and measurement of heterogeneous wireless and wired networks
November 2013
226 pages
ISBN:9781450323710
DOI:10.1145/2512840
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 03 November 2013

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Author Tags

  1. asymmetry
  2. contention window
  3. csma/ca
  4. fairness

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PM2HW2N '13 Paper Acceptance Rate 30 of 115 submissions, 26%;
Overall Acceptance Rate 74 of 226 submissions, 33%

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