A Modified Shuffled Frog Leaping Algorithm for PAPR Reduction in OFDM Systems

Zhou, J; Dutkiewicz, E; Liu, RP; Huang, X; Fang, G; Liu, Y

A Modified Shuffled Frog Leaping Algorithm for PAPR Reduction in OFDM Systems

Zhou, J Dutkiewicz, E

Liu, RP

Huang, X

Fang, G Liu, Y

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Broadcasting, 2015, 61 (4), pp. 698 - 709
Issue Date:: 2015-12-01

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Field	Value	Language
dc.contributor.author	Zhou, J	en_US
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286	en_US
dc.contributor.author	Liu, RP https://orcid.org/0000-0001-7001-6305	en_US
dc.contributor.author	Huang, X https://orcid.org/0000-0001-6869-5732	en_US
dc.contributor.author	Fang, G	en_US
dc.contributor.author	Liu, Y	en_US
dc.date.issued	2015-12-01	en_US
dc.identifier.citation	IEEE Transactions on Broadcasting, 2015, 61 (4), pp. 698 - 709	en_US
dc.identifier.issn	0018-9316	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43228
dc.description.abstract	© 2015 IEEE. Significant reduction of the peak-to-average power ratio (PAPR) is an implementation challenge in orthogonal frequency division multiplexing (OFDM) systems. One way to reduce PAPR is to apply a set of selected partial transmission sequence (PTS) to the transmit signals. However, PTS selection is a highly complex NP-hard problem and the computational complexity is very high when a large number of subcarriers are used in the OFDM system. In this paper, we propose a new heuristic PTS selection method, the modified chaos clonal shuffled frog leaping algorithm (MCCSFLA). MCCSFLA is inspired by natural clonal selection of a frog colony, it is based on the chaos theory. We also analyze MCCSFLA using the Markov chain theory and prove that the algorithm can converge to the global optimum. Simulation results show that the proposed algorithm achieves better PAPR reduction than using others genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms.	en_US
dc.relation.ispartof	IEEE Transactions on Broadcasting	en_US
dc.relation.isbasedon	10.1109/TBC.2015.2459660	en_US
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	A Modified Shuffled Frog Leaping Algorithm for PAPR Reduction in OFDM Systems	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	61	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 - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	61	en_US

Abstract:

© 2015 IEEE. Significant reduction of the peak-to-average power ratio (PAPR) is an implementation challenge in orthogonal frequency division multiplexing (OFDM) systems. One way to reduce PAPR is to apply a set of selected partial transmission sequence (PTS) to the transmit signals. However, PTS selection is a highly complex NP-hard problem and the computational complexity is very high when a large number of subcarriers are used in the OFDM system. In this paper, we propose a new heuristic PTS selection method, the modified chaos clonal shuffled frog leaping algorithm (MCCSFLA). MCCSFLA is inspired by natural clonal selection of a frog colony, it is based on the chaos theory. We also analyze MCCSFLA using the Markov chain theory and prove that the algorithm can converge to the global optimum. Simulation results show that the proposed algorithm achieves better PAPR reduction than using others genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms.

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http://hdl.handle.net/10453/43228