Next Article in Journal
The Choice of an Appropriate Information Dissimilarity Measure for Hierarchical Clustering of River Streamflow Time Series, Based on Calculated Lyapunov Exponent and Kolmogorov Measures
Previous Article in Journal
Attack Algorithm for a Keystore-Based Secret Key Generation Method
Previous Article in Special Issue
Amplitude Constrained MIMO Channels: Properties of Optimal Input Distributions and Bounds on the Capacity
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Entropy 2019, 21(2), 213; https://doi.org/10.3390/e21020213

Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding

1
College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China
2
Department of Electrical System of Launch Vehicle, Institute of Aerospace System Engineering Shanghai, Shanghai Academy of Spaceflight Technology, Shanghai 201109, China
3
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada
*
Author to whom correspondence should be addressed.
Received: 10 January 2019 / Revised: 17 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
(This article belongs to the Special Issue Information Theory for Data Communications and Processing)
Full-Text   |   PDF [327 KB, uploaded 23 February 2019]   |  

Abstract

Consider a symmetric multivariate Gaussian source with components, which are corrupted by independent and identically distributed Gaussian noises; these noisy components are compressed at a certain rate, and the compressed version is leveraged to reconstruct the source subject to a mean squared error distortion constraint. The rate-distortion analysis is performed for two scenarios: centralized encoding (where the noisy source components are jointly compressed) and distributed encoding (where the noisy source components are separately compressed). It is shown, among other things, that the gap between the rate-distortion functions associated with these two scenarios admits a simple characterization in the large limit. View Full-Text
Keywords: CEO problem; mean squared error; multiterminal source coding; rate-distortion; remote source coding CEO problem; mean squared error; multiterminal source coding; rate-distortion; remote source coding
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Wang, Y.; Xie, L.; Zhou, S.; Wang, M.; Chen, J. Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding. Entropy 2019, 21, 213.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top