Gaussian Approximation of Quantization Error for Estimation from Compressed Data

Published

Conference Paper

© 2019 IEEE. We consider the statistical connection between the quantized representation of a high dimensional signal X using a random spherical code and the observation of X under an additive white Gaussian noise (AWGN). We show that given X, the conditional Wasserstein distance between its bitrate-R quantized version and its observation under AWGN of signal-to-noise ratio 22R - 1 is sub-linear in the problem dimension. We then utilize this fact to connect the mean squared error (MSE) attained by an estimator based on an AWGN-corrupted version of X to the MSE attained by the same estimator when fed with its bitrate-R quantized version.

Full Text

Duke Authors

Cited Authors

  • Kipnis, A; Reeves, G

Published Date

  • July 1, 2019

Published In

Volume / Issue

  • 2019-July /

Start / End Page

  • 2029 - 2033

International Standard Serial Number (ISSN)

  • 2157-8095

International Standard Book Number 13 (ISBN-13)

  • 9781538692912

Digital Object Identifier (DOI)

  • 10.1109/ISIT.2019.8849826

Citation Source

  • Scopus