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Exploiting multi-user diversity in wireless LANs with channel-aware CSMA/CA

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conference contribution
posted on 24.05.2016 by Xiaowei Wang, Mahsa Derakhshani, Tho Le-Ngoc
This paper presents a channel-aware access scheme for Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) aiming to take advantage of multi-user diversity and improve throughput, while supporting distributed and asynchronous operation. By dynamically adjusting the contention window of each station (STA) according to its channel state, this method prioritizes STAs that gain most from using a channel, and hence, enhances channel utilization in comparison with a simple random access scheme. To model the proposed Adaptive CSMA/CA (A-CSMA/CA) protocol, a three-dimensional Markov chain is developed. With the aid of such model, performance of the proposed A-CSMA/CA is analytically studied in terms of saturation throughput. Furthermore, illustrative results confirm that A-CSMA/CA significantly improves the throughput, specifically in a large network.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC

Volume

2015-December

Pages

1227 - 1232

Citation

WANG, X., DERAKHSHANI, M. and LE-NGOC, T., 2015. Exploiting multi-user diversity in wireless LANs with channel-aware CSMA/CA. IN: Proceedings of 2015 IEEE 26th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2015), Hong Kong, China, 30 August-2 September 2015, pp.1227-1232.

Publisher

© IEEE

Version

AM (Accepted Manuscript)

Publication date

2015

Notes

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

ISBN

9781467367820

ISSN

2166-9589

Language

en

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