Improvement of Throughput Prediction Scheme Considering Terminal Distribution in Multi-Rate WLAN Considering Both CSMA/CA and Frame Collision

Ryo HAMAMOTO  Chisa TAKANO  Hiroyasu OBATA  Kenji ISHIDA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.12   pp.2923-2933
Publication Date: 2016/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016PAP0019
Type of Manuscript: Special Section PAPER (Special Section on Parallel and Distributed Computing and Networking)
Category: Wireless system
Keyword: 
wireless LAN,  access point,  multi-rate,  throughput prediction,  network design,  

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Summary: 
Wireless Local Area Networks (WLANs) based on the IEEE 802.11 standard have been increasingly used. Access Points (APs) are being established in various public places, such as railway stations and airports, as well as private residences. Moreover, the rate of public WLAN services continues to increase. Throughput prediction of an AP in a multi-rate environment, i.e., predicting the amount of receipt data (including retransmission packets at an AP), is an important issue for wireless network design. Moreover, it is important to solve AP placement and selection problems. To realize the throughput prediction, we have proposed an AP throughput prediction method that considers terminal distribution. We compared the predicted throughput of the proposed method with a method that uses linear order computation and confirmed the performance of the proposed method, not by a network simulator but by the numerical computation. However, it is necessary to consider the impact of CSMA/CA in the MAC layer, because throughput is greatly influenced by frame collision. In this paper, we derive an effective transmission rate considering CSMA/CA and frame collision. We then compare the throughput obtained using the network simulator NS2 with a prediction value calculated by the proposed method. Simulation results show that the maximum relative error of the proposed method is approximately 6% and 15% for UDP and TCP, respectively, while that is approximately 17% and 21% in existing method.