USF Spectral Estimation: Prevalence of Gaussian Cram\'er-Rao Bounds Despite Modulo Folding

TL;DR

This paper derives the Cramér-Rao Bounds for spectral estimation using modulo-folded samples in the USF framework, revealing they are scaled Gaussian CRBs, and validates these bounds through numerical experiments.

Contribution

It introduces the theoretical CRB analysis for USF-based spectral estimation with folded samples, a novel contribution in this field.

Findings

CRBs for USF-SpecEst are scaled versions of Gaussian CRBs

Numerical experiments validate the derived bounds

Provides a benchmark for practical USF-SpecEst deployment

Abstract

Spectral Estimation (SpecEst) is a core area of signal processing with a history spanning two centuries and applications across various fields. With the advent of digital acquisition, SpecEst algorithms have been widely applied to tasks like frequency super-resolution. However, conventional digital acquisition imposes a trade-off: for a fixed bit budget, one can optimize either signal dynamic range or digital resolution (noise floor), but not both simultaneously. The Unlimited Sensing Framework (USF) overcomes this limitation using modulo non-linearity in analog hardware, enabling a novel approach to SpecEst (USF-SpecEst). However, USF-SpecEst requires new theoretical and algorithmic developments to handle folded samples effectively. In this paper, we derive the Cram\'er-Rao Bounds (CRBs) for SpecEst with noisy modulo-folded samples and reveal a surprising result: the CRBs for USF-SpecEst are scaled versions of the Gaussian CRBs for conventional samples. Numerical experiments validate these bounds, providing a benchmark for USF-SpecEst and facilitating its practical deployment.