Zero delay joint source channel coding for multivariate Gaussian sources over orthogonal Gaussian channels
type: Item
instance of: scholarly article
title: Zero delay joint source channel coding for multivariate Gaussian sources over orthogonal Gaussian channels
zbMATH Open document ID: 1325.94078
author: Pål Anders Floor
author: Anna N. Kim
author: T. A. Ramstad
author: Ilangko Balasingham
published in: Entropy
publication date: 2014-09-19T00:00:00Z
review text: Summary: Communication of a multivariate Gaussian source transmitted over orthogonal additive white Gaussian noise channels using delay-free joint source channel codes (JSCC) is studied in this paper. Two scenarios are considered: (1) all components of the multivariate Gaussian are transmitted by one encoder as a vector or several ideally collaborating nodes in a network; (2) the multivariate Gaussian is transmitted through distributed nodes in a sensor network. In both scenarios, the goal is to recover all components of the multivariate Gaussian at the receiver. The paper investigates a subset of JSCC consisting of direct source-to-channel mappings that operate on a symbol-by-symbol basis to ensure zero coding delay. A theoretical analysis that helps explain and quantify distortion behavior for such JSCC is given. Relevant performance bounds for the network are also derived with no constraints on complexity and delay. Optimal linear schemes for both scenarios are presented. Results for Scenario 1 show that linear mappings perform well, except when correlation is high. In Scenario 2, linear mappings provide no gain from correlation when the channel signal-to-noise ratio (SNR) gets large. The gap to the performance upper bound is large for both scenarios, regardless of SNR, when the correlation is high. The main contribution of this paper is the investigation of nonlinear mappings for both scenarios. It is shown that nonlinear mappings can provide substantial gain compared to optimal linear schemes when correlation is high. Contrary to linear mappings for Scenario 2, carefully chosen nonlinear mappings provide a gain for all SNR, as long as the correlation is close to one. Both linear and nonlinear mappings are robust against variations in SNR.
Mathematics Subject Classification ID: 94A29
Mathematics Subject Classification ID: 94A05
Mathematics Subject Classification ID: 94A40
zbMATH DE Number: 6346154
zbMATH Keywords: joint source channel coding
zbMATH Keywords: multivariate Gaussian
zbMATH Keywords: orthogonal channels
zbMATH Keywords: correlation
zbMATH Keywords: zero-delay
zbMATH Keywords: sensor networks
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e15062129
OpenAlex ID: W2008871909
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DOI: 10.3390/E15062129
description: scientific article; zbMATH DE number 6346154