On the Feedback Capacity of Power Constrained Gaussian Noise Channels with Memory

dc.creatorYang, Shaohua
dc.creatorKavcic, Aleksandar
dc.creatorTatikonda, Sekhar
dc.date2007-01-16
dc.date.accessioned2026-07-07T08:16:58Z
dc.date.available2026-07-07T08:16:58Z
dc.descriptionFor a stationary additive Gaussian-noise channel with a rational noise power spectrum of a finite-order $L$, we derive two new results for the feedback capacity under an average channel input power constraint. First, we show that a very simple feedback-dependent Gauss-Markov source achieves the feedback capacity, and that Kalman-Bucy filtering is optimal for processing the feedback. Based on these results, we develop a new method for optimizing the channel inputs for achieving the Cover-Pombra block-length-$n$ feedback capacity by using a dynamic programming approach that decomposes the computation into $n$ sequentially identical optimization problems where each stage involves optimizing $O(L^2)$ variables. Second, we derive the explicit maximal information rate for stationary feedback-dependent sources. In general, evaluating the maximal information rate for stationary sources requires solving only a few equations by simple non-linear programming. For first-order autoregressive and/or moving average (ARMA) noise channels, this optimization admits a closed form maximal information rate formula. The maximal information rate for stationary sources is a lower bound on the feedback capacity, and it equals the feedback capacity if the long-standing conjecture, that stationary sources achieve the feedback capacity, holds.
dc.descriptionTransaction on Information Theory, accepted version, first version submitted on Oct 22, 2003
dc.identifierhttps://arxiv.org/abs/cs/0701099
dc.identifierhttp://arxiv.org/abs/cs/0701099
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/133974
dc.subjectInformation Theory
dc.titleOn the Feedback Capacity of Power Constrained Gaussian Noise Channels with Memory
dc.typetext

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