Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/38057
Title: MMSE recursive estimation of high phase-noise that is Wiener non-stationary
Authors: Su, YT
Wong, KT 
Ho, RKP
Keywords: Least mean squares methods
Phase noise
Recursive estimation
Signal detection
Stochastic processes
Issue Date: 2009
Source: IEEE Radar Conference, 2009 : RadarCon 09 ; 4 - 8 May 2009, Pasadena, CA, USA, p. 1-5 (CD) How to cite?
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 signalto-(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.
URI: http://hdl.handle.net/10397/38057
ISBN: 978-1-4244-2870-0
978-1-4244-2871-7 (E-ISBN)
DOI: 10.1109/RADAR.2009.4976966
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