Application of Neural Networks on Rate Adaptation in IEEE 802.11 WLAN with Multiples Nodes
The paper presents an adaptive Auto Rate Fallback (ARF) scheme to improve tie 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 canse 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. Simulation results demonstrate the effectiveness of the propose algorithm to improve the performance of aggregate throughput in a variety of 802.11 WLAN environments.
Chiapin Wang Jungyi Hsu Kueihsiang Liang Tientsung Tai
Department of Applied Electronic Technology, National Taiwan Normal University, Taipei, Taiwan Graduate Institute of Industrial Education, National Taiwan Normal University, Taipei, Taiwan
国际会议
成都
英文
425-430
2010-07-07（万方平台首次上网日期，不代表论文的发表时间）