Publication

A tight bound on the performance of a minimal-delay joint source-channel coding scheme

Bixio Rimoldi, Marius Kleiner
2010
Conference Papers

Abstract

An analog source is to be transmitted across a Gaussian channel in more than one channel use per source symbol. This paper derives a lower bound on the asymptotic mean squared error for a strategy that consists of repeatedly quantizing the source, transmitting the quantizer outputs in the first channel uses, and sending the remaining quantization error uncoded in the last channel use. The bound coincides with the performance achieved by a suboptimal decoder studied by the authors in a previous paper, thereby establishing that the bound is tight.

Official source

https://infoscience.epfl.ch/entities/publication/c25a12e6-c532-42f3-96b6-0ee531bf4a12

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Bixio Rimoldi, Marius Kleiner
2010
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/c25a12e6-c532-42f3-96b6-0ee531bf4a12

About this result

Related concepts (24)

Mean squared error

In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive (and not zero) is because of randomness or because the estimator does not account for information that could produce a more accurate estimate.

Mean squared prediction error

In statistics the mean squared prediction error (MSPE), also known as mean squared error of the predictions, of a smoothing, curve fitting, or regression procedure is the expected value of the squared prediction errors (PE), the square difference between the fitted values implied by the predictive function and the values of the (unobservable) true value g. It is an inverse measure of the explanatory power of and can be used in the process of cross-validation of an estimated model.

Root-mean-square deviation

The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed. The RMSD represents the square root of the second sample moment of the differences between predicted values and observed values or the quadratic mean of these differences. These deviations are called residuals when the calculations are performed over the data sample that was used for estimation and are called errors (or prediction errors) when computed out-of-sample.

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