Abstract
Maximum likelihood estimation techniques demonstrate performance breakdown at low signal-to-noise ratios where observed estimation errors rapidly depart from the Cramer-Rao bound below a threshold SNR. Rather than rely on the classic asymptotic analysis for prediction of that threshold, random matrix theory (RMT) analysis is employed. Both analytic predictions and direct Monte-Carlo simulations demonstrate that the threshold value can be reliably predicted even for small sample support far removed from classic asymptotic assumptions.
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