High sampling rate or high resolution in a sub-Nyquist sampling system

•Relation model between RSNR and noise folding is derived in CS sampling system.•Relation model between RSNR and quantization bits is derived in CS sampling system.•The tradeoff between sampling rate and number of quantization bits is investigated.•The derived model has been evaluated with satisfact...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation 2020-12, Vol.166, p.108175, Article 108175
Main Authors: Zhao, Yijiu, Wang, Houjun, Zheng, Yanze, Zhuang, Yi, Zhou, Naixin
Format: Article
Language:English
Subjects:
Analog to digital conversion
Analog to digital converters
Compressed sensing
Data smoothing
High resolution
Input noise
Measurement
Noise
Quantization bits
Quantization noise
Sampling
Signal processing
Signal reconstruction
Signal to noise ratio
Time-interleaved sampling
Tradeoffs
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Summary:•Relation model between RSNR and noise folding is derived in CS sampling system.•Relation model between RSNR and quantization bits is derived in CS sampling system.•The tradeoff between sampling rate and number of quantization bits is investigated.•The derived model has been evaluated with satisfactory results. A digital signal acquisition system consists of two steps: sampling and quantization. Sampling maps a continuous signal to a digital signal, which then is quantized into a finite number of bits. Generally, a high sampling rate can ensure robustness to noise, while high resolution means less distortion. However, an analog-to-digital converter (ADC) cannot provide a high sampling rate and high resolution simultaneously. The bit rate is constrained, and there is a tradeoff between sampling rate and resolution. In this paper, we investigate the signal reconstruction in the framework of a compressed sensing based sub-Nyquist sampling system. We also study the noise introduced in the sampling stage and the quantization stage and evaluate the recovered signal-to-noise ratio (RSNR) with respect to the sampling rate and resolution. Considering potential application, we study the tradeoff involved in choosing the sampling rate and number of quantization bits according to the input SNR. Finally, we derive a relationship between RSNR and signal sparsity order, sampling rate, and number of quantization bits.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2020.108175