Application of Neural Networks on Rate Adaptation in IEEE 802.11 WLAN with Multiples Nodes

Abstract

The paper presents an adaptive Auto Rate Fallback (ARF) scheme to improve the performance of aggregate throughput in IEEE 802.11 Wireless Local Area Network (WLAN) with multiple nodes. When the number of contending nodes increases, using ARF will be likely to degrade transmission rates due to increasing packet collisions and can consequently cause a decline of the overall throughput. In this paper we propose a neural-network based adaptive ARF scheme which improves the throughput performance by dynamically adjusting the system parameters that determine the transmission rates according to the contention situations including the amount of contending nodes and traffic intensity. The performance of our scheme is evaluated and compared with that of other LA schemes by using the Qualnet simulator. Simulator results demonstrate the effectiveness of the propose algorithm to improve the performance of aggregate throughput in a variety of 802.11 WLAN environments.