MMSE recursive estimation of high phase-noise that is Wiener non-stationary

Yeong Tzay Su; Kainam Thomas Wong; Keang Po Ricky Ho

doi:10.1109/RADAR.2009.4976966

MMSE recursive estimation of high phase-noise that is Wiener non-stationary

Yeong Tzay Su, Kainam Thomas Wong, Keang Po Ricky Ho

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

To estimate Wiener phase noise of arbitrarily large magnitude (relative to the symbol duration), this work pioneers a linear minimum-mean-square error (LMMSE) discrete-time estimator. This proposed estimator may be pre-set to any arbitrary number of taps and any arbitrary latency. The coefficients of this linear estimator depend only on the values of the signal-to-(additive)-noise ratio and the phase-noise variance. Moreover, rigorous analysis here (1) proves that this sequence of LMMSE-weights are unimodal when plotted against the weight-index, (2) derives an upper bound and a lower bound, in closed forms, for the LMMSE-weights, and (3) proves that this sequence of LMMSE-weights converges to be Laplacian when plotted against the weight-index, as the number of taps approaches infinity.

Original language	English
Title of host publication	2009 IEEE Radar Conference, RADAR 2009
DOIs	https://doi.org/10.1109/RADAR.2009.4976966
Publication status	Published - 11 Sept 2009
Event	2009 IEEE Radar Conference, RADAR 2009 - Pasadena, CA, United States Duration: 4 May 2009 → 8 May 2009

Conference

Conference	2009 IEEE Radar Conference, RADAR 2009
Country/Territory	United States
City	Pasadena, CA
Period	4/05/09 → 8/05/09

ASJC Scopus subject areas

Electrical and Electronic Engineering

More information

10.1109/RADAR.2009.4976966

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title = "MMSE recursive estimation of high phase-noise that is Wiener non-stationary",

abstract = "To estimate Wiener phase noise of arbitrarily large magnitude (relative to the symbol duration), this work pioneers a linear minimum-mean-square error (LMMSE) discrete-time estimator. This proposed estimator may be pre-set to any arbitrary number of taps and any arbitrary latency. The coefficients of this linear estimator depend only on the values of the signal-to-(additive)-noise ratio and the phase-noise variance. Moreover, rigorous analysis here (1) proves that this sequence of LMMSE-weights are unimodal when plotted against the weight-index, (2) derives an upper bound and a lower bound, in closed forms, for the LMMSE-weights, and (3) proves that this sequence of LMMSE-weights converges to be Laplacian when plotted against the weight-index, as the number of taps approaches infinity.",

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AU - Su, Yeong Tzay

AU - Wong, Kainam Thomas

AU - Ho, Keang Po Ricky

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Y1 - 2009/9/11

N2 - To estimate Wiener phase noise of arbitrarily large magnitude (relative to the symbol duration), this work pioneers a linear minimum-mean-square error (LMMSE) discrete-time estimator. This proposed estimator may be pre-set to any arbitrary number of taps and any arbitrary latency. The coefficients of this linear estimator depend only on the values of the signal-to-(additive)-noise ratio and the phase-noise variance. Moreover, rigorous analysis here (1) proves that this sequence of LMMSE-weights are unimodal when plotted against the weight-index, (2) derives an upper bound and a lower bound, in closed forms, for the LMMSE-weights, and (3) proves that this sequence of LMMSE-weights converges to be Laplacian when plotted against the weight-index, as the number of taps approaches infinity.

AB - To estimate Wiener phase noise of arbitrarily large magnitude (relative to the symbol duration), this work pioneers a linear minimum-mean-square error (LMMSE) discrete-time estimator. This proposed estimator may be pre-set to any arbitrary number of taps and any arbitrary latency. The coefficients of this linear estimator depend only on the values of the signal-to-(additive)-noise ratio and the phase-noise variance. Moreover, rigorous analysis here (1) proves that this sequence of LMMSE-weights are unimodal when plotted against the weight-index, (2) derives an upper bound and a lower bound, in closed forms, for the LMMSE-weights, and (3) proves that this sequence of LMMSE-weights converges to be Laplacian when plotted against the weight-index, as the number of taps approaches infinity.

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DO - 10.1109/RADAR.2009.4976966

M3 - Conference article published in proceeding or book

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BT - 2009 IEEE Radar Conference, RADAR 2009

T2 - 2009 IEEE Radar Conference, RADAR 2009

Y2 - 4 May 2009 through 8 May 2009

ER -

MMSE recursive estimation of high phase-noise that is Wiener non-stationary

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