Optimal Quality-of-Service Scheduling for Energy-Harvesting Powered Wireless Communications

Chen, X; Ni, W; Wang, X; Sun, Y

Optimal Quality-of-Service Scheduling for Energy-Harvesting Powered Wireless Communications

Chen, X Ni, W

Wang, X Sun, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Wireless Communications, 2016, 15, (5), pp. 3269-3280
Issue Date:: 2016-05-01

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Field	Value	Language
dc.contributor.author	Chen, X
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Wang, X
dc.contributor.author	Sun, Y
dc.date.accessioned	2022-08-19T00:52:45Z
dc.date.available	2022-08-19T00:52:45Z
dc.date.issued	2016-05-01
dc.identifier.citation	IEEE Transactions on Wireless Communications, 2016, 15, (5), pp. 3269-3280
dc.identifier.issn	1536-1276
dc.identifier.issn	1558-2248
dc.identifier.uri	http://hdl.handle.net/10453/160473
dc.description.abstract	In this paper, a new dynamic string tautening algorithm is proposed to generate the most energy-efficient off-line schedule for delay-limited traffic of transmitters with non-negligible circuit power. The algorithm is based on two key findings that we derive through judicious convex formulation and resultant optimality conditions, specifies a set of simple but optimal rules, and generates the optimal schedule with a low complexity of O(N2) in the worst case. The proposed algorithm is also extended to on-line scenarios, where the transmit schedule is generated on-the-fly. Simulation shows that the proposed algorithm requires substantially lower average complexity by almost two orders of magnitude to retain optimality than general convex solvers. The effective transmit region, specified by the tradeoff of the data arrival rate and the energy harvesting rate, is substantially larger using our algorithm than using other existing alternatives. Significantly more data or less energy can be supported in the proposed algorithm.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Wireless Communications
dc.relation.isbasedon	10.1109/TWC.2016.2519411
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Optimal Quality-of-Service Scheduling for Energy-Harvesting Powered Wireless Communications
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
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
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-19T00:52:44Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	5

Abstract:

In this paper, a new dynamic string tautening algorithm is proposed to generate the most energy-efficient off-line schedule for delay-limited traffic of transmitters with non-negligible circuit power. The algorithm is based on two key findings that we derive through judicious convex formulation and resultant optimality conditions, specifies a set of simple but optimal rules, and generates the optimal schedule with a low complexity of O(N2) in the worst case. The proposed algorithm is also extended to on-line scenarios, where the transmit schedule is generated on-the-fly. Simulation shows that the proposed algorithm requires substantially lower average complexity by almost two orders of magnitude to retain optimality than general convex solvers. The effective transmit region, specified by the tradeoff of the data arrival rate and the energy harvesting rate, is substantially larger using our algorithm than using other existing alternatives. Significantly more data or less energy can be supported in the proposed algorithm.

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http://hdl.handle.net/10453/160473