Feedback Capacity of the First-Order Moving Average Gaussian Channel
Young-Han Kim
Abstract
The feedback capacity of the stationary Gaussian additive noise channel has been open, except for the case where the noise is white. Here we find the feedback capacity of the stationary first-order moving average additive Gaussian noise channel in closed form. Specifically, the channel is given by Yi = Xi + Zi, i = 1, 2, ..., where the input \Xi\ satisfies a power constraint and the noise \Zi\ is a first-order moving average Gaussian process defined by Zi = αUi-1 + Ui, |α| 1, with white Gaussian innovations Ui, i = 0,1,.... We show that the feedback capacity of this channel is - x0, where x0 is the unique positive root of the equation ρx2 = (1-x2) (1 - |α|x)2, and ρ is the ratio of the average input power per transmission to the variance of the noise innovation Ui. The optimal coding scheme parallels the simple linear signalling scheme by Schalkwijk and Kailath for the additive white Gaussian noise channel -- the transmitter sends a real-valued information-bearing signal at the beginning of communication and subsequently refines the receiver's error by processing the feedback noise signal through a linear stationary first-order autoregressive filter. The resulting error probability of the maximum likelihood decoding decays doubly-exponentially in the duration of the communication. This feedback capacity of the first-order moving average Gaussian channel is very similar in form to the best known achievable rate for the first-order autoregressive Gaussian noise channel studied by Butman, Wolfowitz, and Tiernan, although the optimality of the latter is yet to be established.
Create a lesson
Related papers
Galois Hulls of Generalized Roth-Lempel Codes and Their Applications to EAQECCs
Xuefei Wu, Qi Liu, Yingchun Chen et al.
Maximum Entropy Probability Distributions on Spheres with Fixed Mean Busemann Function and Holomorphic-Information-Geometric Model of Cognition
Vladimir Jacimovic
Norm-One Torus Decompositions and Decoding of Gashkov-Sidel'nikov Codes
Minjia Shi, Shitao Li, Yuhong Xia et al.
A Mirror Vanishing Band for Weight Distributions of Binary Linear Codes
Xianmang He
Tri-Hybrid Beamforming Design for Large-Scale MIMO ISAC Systems
Tianyu Fang, Mengyuan Ma, Markku Juntti et al.
Equivalence Between Nested Gibbs Measures and Log-Linear Combinations of Gibbs Measures
Yaiza Bermudez, Samir M. Perlaza, Iñaki Esnaola