On Noise-Sensitivity of Unlimited Sampling in Line Spectral Estimation

The unlimited sampling framework, utilizing modulo analog-to-digital converters (ADCs), has recently been introduced to mitigate the information loss caused by the dynamic range limitations of traditional ADCs. In this paper, we evaluate the noise sensitivity of unlimited sampling in line spectral estimation by deriving the Cramér–Rao bound (CRB) and show that estimation based solely the modulo measurements can be sensitive to noise. To address this issue, we propose integrating the modulo ADC-based unlimited sampling with the sign information of the original signal. We compare the CRBs of different unlimited sampling frameworks and show the benefit of enhanced robustness to noise by the sign-aided approach.

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Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10889380
Primary Topic
Target Tracking and Data Fusion in Sensor Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

On Noise-Sensitivity of Unlimited Sampling in Line Spectral Estimation

Zai Yang, Wenlong Wang
Target Tracking and Data Fusion in Sensor Networks
article

On Noise-Sensitivity of Unlimited Sampling in Line Spectral Estimation

Zai Yang, Wenlong Wang
article en

Abstract

The unlimited sampling framework, utilizing modulo analog-to-digital converters (ADCs), has recently been introduced to mitigate the information loss caused by the dynamic range limitations of traditional ADCs. In this paper, we evaluate the noise sensitivity of unlimited sampling in line spectral estimation by deriving the Cramér–Rao bound (CRB) and show that estimation based solely the modulo measurements can be sensitive to noise. To address this issue, we propose integrating the modulo ADC-based unlimited sampling with the sign information of the original signal. We compare the CRBs of different unlimited sampling frameworks and show the benefit of enhanced robustness to noise by the sign-aided approach.

Xi'an Jiaotong University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 98%
Target Tracking and Data Fusion in Sensor Networks
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