Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (2)

Search Parameters:
Keywords = wide sense stationary (WSS) vector source

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
20 pages, 433 KiB  
Article
On the Asymptotic Optimality of a Low-Complexity Coding Strategy for WSS, MA, and AR Vector Sources
by Jesús Gutiérrez-Gutiérrez, Marta Zárraga-Rodríguez and Xabier Insausti
Entropy 2020, 22(12), 1378; https://doi.org/10.3390/e22121378 - 5 Dec 2020
Cited by 4 | Viewed by 1577
Abstract
In this paper, we study the asymptotic optimality of a low-complexity coding strategy for Gaussian vector sources. Specifically, we study the convergence speed of the rate of such a coding strategy when it is used to encode the most relevant vector sources, namely [...] Read more.
In this paper, we study the asymptotic optimality of a low-complexity coding strategy for Gaussian vector sources. Specifically, we study the convergence speed of the rate of such a coding strategy when it is used to encode the most relevant vector sources, namely wide sense stationary (WSS), moving average (MA), and autoregressive (AR) vector sources. We also study how the coding strategy considered performs when it is used to encode perturbed versions of those relevant sources. More precisely, we give a sufficient condition for such perturbed versions so that the convergence speed of the rate remains unaltered. Full article
(This article belongs to the Section Information Theory, Probability and Statistics)
Show Figures

Figure 1

22 pages, 503 KiB  
Article
A Low-Complexity and Asymptotically Optimal Coding Strategy for Gaussian Vector Sources
by Marta Zárraga-Rodríguez, Jesús Gutiérrez-Gutiérrez and Xabier Insausti
Entropy 2019, 21(10), 965; https://doi.org/10.3390/e21100965 - 2 Oct 2019
Cited by 2 | Viewed by 2099
Abstract
In this paper, we present a low-complexity coding strategy to encode (compress) finite-length data blocks of Gaussian vector sources. We show that for large enough data blocks of a Gaussian asymptotically wide sense stationary (AWSS) vector source, the rate of the coding strategy [...] Read more.
In this paper, we present a low-complexity coding strategy to encode (compress) finite-length data blocks of Gaussian vector sources. We show that for large enough data blocks of a Gaussian asymptotically wide sense stationary (AWSS) vector source, the rate of the coding strategy tends to the lowest possible rate. Besides being a low-complexity strategy it does not require the knowledge of the correlation matrix of such data blocks. We also show that this coding strategy is appropriate to encode the most relevant Gaussian vector sources, namely, wide sense stationary (WSS), moving average (MA), autoregressive (AR), and ARMA vector sources. Full article
(This article belongs to the Section Information Theory, Probability and Statistics)
Show Figures

Figure 1

Back to TopTop