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Quantizer-based suboptimal detectors: noise-enhanced performance and robustness

机译:基于量化器的次优检测器:噪声增强的性能和鲁棒性

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The goal of the paper is the study of suboptimal quantizer based detectors. We place ourselves in the situation where internal noise is present in the hard implementation of the thresholds. We hence focus on the study of random quantizers, showing that they present the noise-enhanced detection property. The random quantizers studied are of two types: time invariant when sampled once for all the observations, time variant when sampled at each time. They are built by adding fluctuations on the thresholds of a uniform quantizer. If the uniform quantizer is matched to the symmetry of the detection problem, adding fluctuation deteriorates the performance. If the uniform quantizer is mismatched, adding noise can improve the performance. Furthermore, we show that the time varying quantizer is better than the time invariant quantizer, and we show that both are more robust than the optimal quantizer. Finally, we introduce the adapted random quantizer for which the levels are chosen in order to approximate the likelihood ratio.
机译:本文的目的是研究基于次优量化器的检测器。我们将自己置于阈值的硬实施中存在内部噪声的情况。因此,我们专注于随机量化器的研究,表明它们具有噪声增强的检测特性。研究的随机量化器有两种类型:对所有观测值采样一次时不变,而在每次采样时时变。通过在统一量化器的阈值上添加波动来构建它们。如果统一量化器与检测问题的对称性匹配,则增加波动会降低性能。如果统一量化器不匹配,则添加噪声可以提高性能。此外,我们证明了时变量化器比时不变量化器要好,并且我们展示了两者均比最佳量化器更健壮。最后,我们介绍一种自适应随机量化器,为其选择级别以近似似然比。

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