Saturation Throughput Analysis of IEEE 802.11b Wireless Local Area Networks under High Interference Considering Capture Effects

TL;DR

This paper presents a new saturation throughput analysis for IEEE 802.11b WLANs that accounts for erroneous channels and capture effects, proposing a mechanism to improve performance under high interference and noisy conditions.

Contribution

It extends Bianchi's Markov chain model to include real channel conditions and capture effects, and introduces a mechanism to enhance throughput in noisy environments.

Findings

The proposed model accurately predicts throughput under high interference.

The new mechanism significantly outperforms existing standards in noisy conditions.

Performance improvements are demonstrated through simulations.

Abstract

Distributed contention based Medium Access Control (MAC) protocols are the fundamental components for IEEE 802.11 based Wireless Local Area Networks (WLANs). Contention windows (CW) change dynamically to adapt to the current contention level, Upon each packet collision, a station doubles its CW to reduce further collision of packets. IEEE 802.11 Distributed Coordination Function (DCF) suffers from a common problem in erroneous channel. They cannot distinguish noise lost packets from collision lost packets. In both situations a station does not receive its ACK and doubles the CW to reduce further packet collisions. This increases backoff overhead unnecessarily in addition to the noise lost packets, reduces the throughput significantly. Furthermore, the aggregate throughput of a practical WLAN strongly depends on the channel conditions. In real radio environment, the received signal power at the access point from a station is subjected to deterministic path loss, shadowing and fast multipath fading. In this paper, we propose a new saturation throughput analysis for IEEE 802.11 DCF considering erroneous channel and capture effects. To alleviate the low performance of IEEE 802.11 DCF, we introduce a mechanism that greatly outperforms under noisy environment with low network traffic and compare their performances to the existing standards. We extend the multidimensional Markov chain model initially proposed by Bianchi(3) to characterize the behavior of DCF in order to account both real channel conditions and capture effects, especially in a high interference radio environment.