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This paper addresses the L-channel multiple descriptions problem for a Gaussian source under mean squared error (MSE) distortion metric. We focus on particular cross-sections of the general rate-distortion region where a subset the 2L - 1 distortion constraints are not imposed. Specifically, we assume that certain descriptions are never received simultaneously at the decoder and thereby the transmitted...
Detection based on eigenvalues of received signal covariance matrix is currently one of the most effective solution for spectrum sensing problem in cognitive radios. However, the results of these schemes always depend on asymptotic assumptions since the close-formed expression of exact eigenvalues ratio distribution is exceptionally complex to compute in practice. In this paper, non-asymptotic spectrum...
Correlation screening is frequently the only practical way to discover dependencies in very high dimensional data. In correlation screening a high threshold is applied to the matrix of sample correlation coefficients of the multivariate data. The variables having coefficients that exceed the threshold are called discoveries and are classified to be dependent. The mean number of discoveries and the...
We obtain a dependence balance based outer bound on the capacity region of the two-user multiple access channel with generalized feedback (MAC-GF). We investigate a Gaussian MAC with user-cooperation (MAC-UC), where each transmitter receives an additive white Gaussian noise corrupted version of the channel input of the other transmitter. For all non-zero values of cooperation noise variances, our...
It is shown that the entropy power of a sum of independent random vectors, seen as a set function, is fractionally superadditive. This resolves a conjecture of the first author and A. R. Barron, and implies in particular all previously known entropy power inequalities for independent random variables. It is also shown that, for general dimension, the entropy power of a sum of independent random vectors...
Given n (discrete or continuous) random variables Xi, the (2n - 1)-dimensional vector obtained by evaluating the joint entropy of all non-empty subsets of {X1,hellip, Xn} is called an entropic vector. Determining the region of entropic vectors is an important open problem in information theory. Recently, Chan has shown that the entropy regions for discrete and continuous random variables, though different,...
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