Algorithmic extensions of Su-Wong-Ho linear MMSE estimator for large-magnitude Levy-process phase-noise

Y. T. Su, Kainam Thomas Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


A new linear minimum-mean-square error (LMMSE) estimator has recently been proposed by Su, Wong and Ho to estimate phase-noise of (possibly) large magnitude, temporal non-stationarity, and Levy distribution (which includes the Wiener distribution as a special case). This estimator can handle many different degrees of latency. The estimator is adjustable to any number of taps, which may be pre-computed offline, based on only the signal-to-(additive)-noise ratio and the phase-noise's characteristic function. This pre-computation requires no matrix inversion. The above estimator is algorithmically extended for more flexibility in latency, to select the optimum estimator-tap support-window from a wider data-observation window, and to handle newly arrived samples in a computationally efficient manner.
Original languageEnglish
Pages (from-to)653-654
Number of pages2
JournalElectronics Letters
Issue number11
Publication statusPublished - 26 May 2011

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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