Analog-to-Digital Compression: A New Paradigm for Converting Signals to Bits

@article{Kipnis2018AnalogtoDigitalCA,
  title={Analog-to-Digital Compression: A New Paradigm for Converting Signals to Bits},
  author={Alon Kipnis and Yonina C. Eldar and Andrea J. Goldsmith},
  journal={IEEE Signal Processing Magazine},
  year={2018},
  volume={35},
  pages={16-39}
}
Processing, storing, and communicating information that originates as an analog signal involves converting this information to bits. This conversion can be described by the combined effect of sampling and quantization, as shown in Figure 1. The digital representation is achieved by first sampling the analog signal to represent it by a set of discretetime samples and then quantizing these samples to a finite number of bits. Traditionally, these two operations are considered separately. The… 
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References

SHOWING 1-10 OF 65 REFERENCES
Fundamental Distortion Limits of Analog-to-Digital Compression
TLDR
The results can be seen as an extension of the classical sampling theorem for bandlimited random processes in the sense that they describe the minimal amount of excess distortion in the reconstruction due to lossy compression of the samples and provide the minimal sampling frequency required in order to achieve this distortion.
A Use of Limit Cycle Oscillations to Obtain Robust Analog-to-Digital Converters
  • J. Candy
  • Computer Science
    IEEE Trans. Commun.
  • 1974
TLDR
High quality analog-to-digital conversions are obtained using simple and inexpensive circuits that require no high-precision components and has many of the desirable properties of classical feedback servomechanisms.
Analog-to-digital converter survey and analysis
  • R. Walden
  • Computer Science
    IEEE J. Sel. Areas Commun.
  • 1999
TLDR
The state-of-the-art of ADCs is surveyed, including experimental converters and commercially available parts, and the distribution of resolution versus sampling rate provides insight into ADC performance limitations.
Optimal trade-off between sampling rate and quantization precision in Sigma-Delta A/D conversion
TLDR
This work analyzes the sampling rate of a Sigma-Delta modulator of arbitrary order and shows that for a signal with a spectrum that is constant over its bandwidth, the optimal sampling rate is either the Nyquist rate or the maximal sampling rate corresponding to the output bitrate.
Spectra of quantized signals
  • W. Bennett
  • Computer Science
    Bell Syst. Tech. J.
  • 1948
TLDR
Quantizing of time, or time division, has found application as a means of multiplexing telephone channels and the more familiar word “sampling” will be used here interchangeably with the rather formidable term “quantization of time”.
Energy-efficient analog-to-digital conversion for ultra-wideband radio
TLDR
These limitations are addressed with both a comprehensive mixed-signal design methodology and new circuits and architectures, as presented in the context of an analog-to-digital converter (ADC) for ultra-wideband (UWB) radio.
Least squares quantization in PCM
  • S. P. Lloyd
  • Computer Science
    IEEE Trans. Inf. Theory
  • 1982
TLDR
The corresponding result for any finite number of quanta is derived; that is, necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy.
Quantizing for minimum distortion
  • J. Max
  • Computer Science
    IRE Trans. Inf. Theory
  • 1960
TLDR
This paper discusses the problem of the minimization of the distortion of a signal by a quantizer when the number of output levels of the quantizer is fixed and an algorithm is developed to simplify their numerical solution.
Sampling, data transmission, and the Nyquist rate
TLDR
It is argued that only stable sampling is meaningful in practice, and it is proved that stable sampling cannot be performed at a rate lower than the Nyquist, and data cannot be transmitted as samples at a Rate of 2W per second, regardless of the location of sampling instants, the nature of the set of frequencies which the signals occupy, or the method of construction.
Rate-distortion performance in coding bandlimited sources by sampling and dithered quantization
TLDR
The rate-distortion characteristics of a scheme for encoding continuous-time band limited stationary sources, with a prescribed band, is considered and it is shown that the mean-square error of the scheme is fixed as long as the product of the sampling period and the quantizer second moment is kept constant.
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1
2
3
4
5
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