Transport capacity of distributed wireless CSMA networks

Yang, T; Mao, G; Zhang, W; Tao, X

Transport capacity of distributed wireless CSMA networks

Yang, T Mao, G

Zhang, W Tao, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Wireless Communications, 2014, 13 (10), pp. 5635 - 5647
Issue Date:: 2014-10-01

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Field	Value	Language
dc.contributor.author	Yang, T	en_US
dc.contributor.author	Mao, G https://orcid.org/0000-0002-3598-4949	en_US
dc.contributor.author	Zhang, W	en_US
dc.contributor.author	Tao, X	en_US
dc.date.issued	2014-10-01	en_US
dc.identifier.citation	IEEE Transactions on Wireless Communications, 2014, 13 (10), pp. 5635 - 5647	en_US
dc.identifier.issn	1536-1276	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33765
dc.description.abstract	© 2014 IEEE. In this paper, we study the transport capacity of large multi-hop wireless CSMA networks. Different from previous studies that rely on the use of a centralized scheduling algorithm and/or a centralized routing algorithm to achieve the optimal capacity scaling law, we show that the optimal capacity scaling law can be achieved using entirely distributed routing and scheduling algorithms. Specifically, we consider a network with nodes Poissonly distributed with unit intensity on a √n x √n Bn ⊂ double-struck R2. Furthermore, each node chooses its destination randomly and independently and transmits following a CSMA protocol. By resorting to the percolation theory and by carefully tuning the three controllable parameters in CSMA protocols, i.e., transmission power, carrier-sensing threshold, and countdown timer, we show that a throughput of Θ (1/ √n) is achievable in distributed CSMA networks. Furthermore, we derive the pre-constant preceding the order of the transport capacity by giving an upper and a lower bound of the transport capacity. The tightness of the bounds is validated using simulations.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP120102030
dc.relation	http://purl.org/au-research/grants/arc/DP110100538
dc.relation.ispartof	IEEE Transactions on Wireless Communications	en_US
dc.relation.isbasedon	10.1109/TWC.2014.2325899	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Transport capacity of distributed wireless CSMA networks	en_US
dc.type	Journal Article
utslib.citation.volume	10	en_US
utslib.citation.volume	13	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access
pubs.issue	10	en_US
pubs.publication-status	Published	en_US
pubs.volume	13	en_US

Abstract:

© 2014 IEEE. In this paper, we study the transport capacity of large multi-hop wireless CSMA networks. Different from previous studies that rely on the use of a centralized scheduling algorithm and/or a centralized routing algorithm to achieve the optimal capacity scaling law, we show that the optimal capacity scaling law can be achieved using entirely distributed routing and scheduling algorithms. Specifically, we consider a network with nodes Poissonly distributed with unit intensity on a √n x √n Bn ⊂ double-struck R2. Furthermore, each node chooses its destination randomly and independently and transmits following a CSMA protocol. By resorting to the percolation theory and by carefully tuning the three controllable parameters in CSMA protocols, i.e., transmission power, carrier-sensing threshold, and countdown timer, we show that a throughput of Θ (1/ √n) is achievable in distributed CSMA networks. Furthermore, we derive the pre-constant preceding the order of the transport capacity by giving an upper and a lower bound of the transport capacity. The tightness of the bounds is validated using simulations.

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http://hdl.handle.net/10453/33765