Please use this identifier to cite or link to this item: https://doi.org/10.1109/LPT.2009.2028580
DC FieldValue
dc.titleParallel implementation of decision-aided maximum-likelihood phase estimation in coherent M-ary phase-shift keying systems
dc.contributor.authorZhang, S.
dc.contributor.authorYu, C.
dc.contributor.authorKam, P.Y.
dc.contributor.authorChen, J.
dc.date.accessioned2014-06-17T03:00:56Z
dc.date.available2014-06-17T03:00:56Z
dc.date.issued2009
dc.identifier.citationZhang, S., Yu, C., Kam, P.Y., Chen, J. (2009). Parallel implementation of decision-aided maximum-likelihood phase estimation in coherent M-ary phase-shift keying systems. IEEE Photonics Technology Letters 21 (19) : 1471-1473. ScholarBank@NUS Repository. https://doi.org/10.1109/LPT.2009.2028580
dc.identifier.issn10411135
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/56985
dc.description.abstractTo make decision-aided (DA) maximum-likelihood (ML) phase estimation suitable for real-time processing, a parallel implementation is proposed. The variance of the phase error using the parallel DA ML is analytically examined. The bit-error-rate performance is also analytically investigated in different M-ary phase-shift keying (PSK) systems. The results show that parallel DA ML can outperform block Mth-power in binary PSK format, while suffering performance penalty originating from parallelism and pipelining in high-order PSK formats. © 2009 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/LPT.2009.2028580
dc.sourceScopus
dc.subjectCoherent detection
dc.subjectDecision-aided maximum likelihood (DA ML)
dc.subjectLaser phase noise
dc.subjectM-ary phase-shift keying (PSK)
dc.subjectParallel processing
dc.subjectPhase estimation
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/LPT.2009.2028580
dc.description.sourcetitleIEEE Photonics Technology Letters
dc.description.volume21
dc.description.issue19
dc.description.page1471-1473
dc.description.codenIPTLE
dc.identifier.isiut000270037200025
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