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

Consider a symmetric multivariate Gaussian source with ℓ 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 er...

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Detalles Bibliográficos
Autores principales: Wang, Yizhong, Xie, Li, Zhou, Siyao, Wang, Mengzhen, Chen, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514694/
https://www.ncbi.nlm.nih.gov/pubmed/33266928
http://dx.doi.org/10.3390/e21020213
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author Wang, Yizhong
Xie, Li
Zhou, Siyao
Wang, Mengzhen
Chen, Jun
author_facet Wang, Yizhong
Xie, Li
Zhou, Siyao
Wang, Mengzhen
Chen, Jun
author_sort Wang, Yizhong
collection PubMed
description Consider a symmetric multivariate Gaussian source with ℓ 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 ℓ limit.
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spelling pubmed-75146942020-11-09 Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding Wang, Yizhong Xie, Li Zhou, Siyao Wang, Mengzhen Chen, Jun Entropy (Basel) Article Consider a symmetric multivariate Gaussian source with ℓ 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 ℓ limit. MDPI 2019-02-23 /pmc/articles/PMC7514694/ /pubmed/33266928 http://dx.doi.org/10.3390/e21020213 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Yizhong
Xie, Li
Zhou, Siyao
Wang, Mengzhen
Chen, Jun
Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding
title Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding
title_full Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding
title_fullStr Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding
title_full_unstemmed Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding
title_short Asymptotic Rate-Distortion Analysis of Symmetric Remote Gaussian Source Coding: Centralized Encoding vs. Distributed Encoding
title_sort asymptotic rate-distortion analysis of symmetric remote gaussian source coding: centralized encoding vs. distributed encoding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514694/
https://www.ncbi.nlm.nih.gov/pubmed/33266928
http://dx.doi.org/10.3390/e21020213
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