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Quantized sampling as sampling with uncertainty in time andamplitude

机译:量化采样是时间和幅度不确定的采样

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In classical sampled quantization, the signal is sampled at discrete times and at discrete values, resulting in uncertainty of the signal amplitude. However, the sampling times and the boundaries of the quantization intervals are still assumed to be known with infinite precision. The aim of this paper is to study quantization and sampling when these quantities are not known with infinite precision by considering quantized sampling in a general framework as sampling with uncertainty in time and amplitude. We define the concept of a valid quantized sample and consider a quantized sampling of a signal as a collection of valid quantized samples. We show that for continuous signals, a set of valid quantized samples generates a secondary set of valid quantized samples. We illustrate that oversampling can reduce reconstruction errors because oversampling can reduce the uncertainty in the secondary quantized samples. In particular, these secondary quantized samples have uncertainty approaching zero as oversampling increases, provided the sampling time and quantization thresholds are known with infinite precision. For a class of T-periodic bandlimited signals, this implies that the reconstruction error is a function of the oversampling ratio, the uncertainty in the sampling time, the stepsize of the quantizer, and the uncertainty in the quantization thresholds
机译:在经典的采样量化中,以离散时间和离散值对信号进行采样,从而导致信号幅度不确定。但是,仍然假定采样时间和量化间隔的边界是无限精确的。本文的目的是通过将通用框架中的量化采样视为具有时间和幅度不确定性的采样,来研究未知数量的采样和量化。我们定义有效量化样本的概念,并将信号的量化样本视为有效量化样本的集合。我们表明,对于连续信号,一组有效的量化样本会生成第二组有效的量化样本。我们说明过采样可以减少重建误差,因为过采样可以减少二次量化样本中的不确定性。特别地,这些二次量化样本的不确定性会随着过采样的增加而接近零,前提是已知采样时间和量化阈值的精度是无限的。对于一类T周期带宽限制信号,这意味着重构误差是过采样率,采样时间的不确定性,量化器的步长以及量化阈值的不确定性的函数

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