2009
DOI: 10.1016/j.sigpro.2009.01.008
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A stochastic analysis of an iterative semi-blind beamformer for TDMA systems

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Cited by 1 publication
(6 citation statements)
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“…This means that in addition to the training signals and known a priori or estimated with the help of the training signals channel propagation matrix H, we can estimate the signal-free interference plus noise covariance matrix Rt on the training interval and the covariance matrix Rd of the mixture of interference and signal on the data interval. Thus, if we confine our adaptive search of the optimal mixing factor δ in (13) only by the second order statistics, no much more measurable parameters are left in our disposal. The main reason is that in terms of their correlation properties, the useful signals on the data interval are indistinguishable from CCI, which is often created by very similar signals.…”
Section: Maximum Likelihood Methodology For Optimization Of the Regul...mentioning
confidence: 99%
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“…This means that in addition to the training signals and known a priori or estimated with the help of the training signals channel propagation matrix H, we can estimate the signal-free interference plus noise covariance matrix Rt on the training interval and the covariance matrix Rd of the mixture of interference and signal on the data interval. Thus, if we confine our adaptive search of the optimal mixing factor δ in (13) only by the second order statistics, no much more measurable parameters are left in our disposal. The main reason is that in terms of their correlation properties, the useful signals on the data interval are indistinguishable from CCI, which is often created by very similar signals.…”
Section: Maximum Likelihood Methodology For Optimization Of the Regul...mentioning
confidence: 99%
“…1) the basic one as in (13) without any "cleaning" marked as "Initial"; 2) the one with "cleaning" of the data interval with the initially estimated desired signal…”
Section: A Basic Scenariomentioning
confidence: 99%
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