An extension of the entropy power inequality to the form $N_r^alpha(X+Y) geq N_r^alpha(X) + N_r^alpha(Y)$ with arbitrary independent summands $X$ and $Y$ in $mathbb{R}^n$ is obtained for the Renyi entropy and powers $alpha geq (r+1)/2$.
Using a sharp version of the reverse Young inequality, and a Renyi entropy comparison result due to Fradelizi, Madiman, and Wang, the authors are able to derive Renyi entropy power inequalities for log-concave random vectors when Renyi parameters belong to $(0,1)$. Furthermore, the estimates are shown to be sharp up to absolute constants.
This paper gives improved R{e}nyi entropy power inequalities (R-EPIs). Consider a sum $S_n = sum_{k=1}^n X_k$ of $n$ independent continuous random vectors taking values on $mathbb{R}^d$, and let $alpha in [1, infty]$. An R-EPI provides a lower bound on the order-$alpha$ Renyi entropy power of $S_n$ that, up to a multiplicative constant (which may depend in general on $n, alpha, d$), is equal to the sum of the order-$alpha$ Renyi entropy powers of the $n$ random vectors ${X_k}_{k=1}^n$. For $alpha=1$, the R-EPI coincides with the well-known entropy power inequality by Shannon. The first improved R-EPI is obtained by tightening the recent R-EPI by Bobkov and Chistyakov which relies on the sharpened Youngs inequality. A further improvement of the R-EPI also relies on convex optimization and results on rank-one modification of a real-valued diagonal matrix.
This paper considers an entropy-power inequality (EPI) of Costa and presents a natural vector generalization with a real positive semidefinite matrix parameter. This new inequality is proved using a perturbation approach via a fundamental relationship between the derivative of mutual information and the minimum mean-square error (MMSE) estimate in linear vector Gaussian channels. As an application, a new extremal entropy inequality is derived from the generalized Costa EPI and then used to establish the secrecy capacity regions of the degraded vector Gaussian broadcast channel with layered confidential messages.
This note contributes to the understanding of generalized entropy power inequalities. Our main goal is to construct a counter-example regarding monotonicity and entropy comparison of weighted sums of independent identically distributed log-concave random variables. We also present a complex analogue of a recent dependent entropy power inequality of Hao and Jog, and give a very simple proof.
The paper establishes the equality condition in the I-MMSE proof of the entropy power inequality (EPI). This is done by establishing an exact expression for the deficit between the two sides of the EPI. Interestingly, a necessary condition for the equality is established by making a connection to the famous Cauchy functional equation.