Modeling and optimization of wireless local area network

  • Authors:

  • Jun Yin;Xiaodong Wang;Dharma P. Agrawal

  • Affiliations:

  • Department of ECECS, OBR Center of Distributed and Mobile Computing, University of Cincinnati, Cincinnati, OH 45221-0030, USA;Department of ECECS, OBR Center of Distributed and Mobile Computing, University of Cincinnati, Cincinnati, OH 45221-0030, USA;Department of ECECS, OBR Center of Distributed and Mobile Computing, University of Cincinnati, Cincinnati, OH 45221-0030, USA

  • Venue:
  • Computer Communications
  • Year:
  • 2005

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Abstract

As wireless local area network technology is gaining popularity, performance analysis and optimization of it becomes more important. However, as compared to wired LAN, wireless channel is error-prone. Most of the existing work on the performance analysis of IEEE 802.11 distributed coordination function (DCF) assumes saturated traffic and ideal channel condition. In this paper, modeling of DCF is analyzed under a general traffic load and variable channel condition. A more realistic and comprehensive model is proposed to optimize the performance of DCF in both ideal and error-prone channels, and for both the basic scheme of DCF and DCF with four-way handshaking. Many factors, such as the number of contending nodes, the traffic load, contention window, packet overhead and channel condition, that affect the throughput and the delay of a wireless network have been incorporated. It is shown that under error-prone environment, a trade-off exists between the desire to reduce the ratio of overhead in the data packet by adopting a larger packet size, and the need to reduce the packet error rate by using a smaller packet length. Based on our analytical model, both the optimal packet size and the optimal minimum contention window are determined under various traffic loads and channel conditions. It is also observed that, in error-prone environments, optimal packet size has more significant improvement on the performance than optimal contention window. Our analytical model is validated via simulations using ns-2.