Estimation of Bandlimited Signals in Additive Gaussian Noise: a "Precision Indifference" Principle

TL;DR

This paper demonstrates that for bandlimited signals in Gaussian noise, the mean-squared error decreases proportionally to the inverse of oversampling ratio, regardless of whether quantization is infinite or one-bit, revealing a quantization precision indifference principle.

Contribution

It introduces a principle showing that signal reconstruction distortion is unaffected by quantizer precision, given sufficient oversampling, in noisy bandlimited signal estimation.

Findings

Distortion decreases as 1/N with oversampling ratio N.

Quantization precision does not affect the fundamental distortion law.

A quantization indifference principle is established for Gaussian noise scenarios.

Abstract

The sampling, quantization, and estimation of a bounded dynamic-range bandlimited signal affected by additive independent Gaussian noise is studied in this work. For bandlimited signals, the distortion due to additive independent Gaussian noise can be reduced by oversampling (statistical diversity). The pointwise expected mean-squared error is used as a distortion metric for signal estimate in this work. Two extreme scenarios of quantizer precision are considered: (i) infinite precision (real scalars); and (ii) one-bit quantization (sign information). If $N$ is the oversampling ratio with respect to the Nyquist rate, then the optimal law for distortion is $O(1/N)$. We show that a distortion of $O(1/N)$ can be achieved irrespective of the quantizer precision by considering the above-mentioned two extreme scenarios of quantization. Thus, a quantization precision indifference principle is discovered, where the reconstruction distortion law, up to a proportionality constant, is unaffected by quantizer's accuracy.