Journal ArticleDOI
Signalling over a Gaussian channel with feedback and autoregressive noise
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This article is published in Journal of Applied Probability.The article was published on 1975-12-01. It has received 31 citations till now. The article focuses on the topics: Noise & Autoregressive model.read more
Citations
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Journal ArticleDOI
Feedback Capacity of Stationary Gaussian Channels
TL;DR: This result shows that the celebrated Schalkwijk-Kailath coding achieves the feedback capacity for the first-order autoregressive moving-average Gaussian channel, positively answering a long-standing open problem studied by Butman, Tiernan-SchalkWijk, Wolfowitz, Ozarow, Ordentlich, Yang-Kavc¿ic¿-Tatikonda, and others.
Posted Content
Feedback Capacity of Stationary Gaussian Channels
TL;DR: In this article, the authors characterized the feedback capacity of additive stationary Gaussian noise channels as the solution to a variational problem and proved that the optimal feedback coding scheme is stationary.
Posted Content
Feedback Capacity of the First-Order Moving Average Gaussian Channel
TL;DR: This work considers another simple special case of the stationary first-order moving average additive Gaussian noise channel and finds the feedback capacity in closed form, which is very similar in form to the best known achievable rate for the first- order autoregressive Gaussia noise channel given by Butman.
Journal ArticleDOI
Feedback capacity of the first-order moving average Gaussian channel
TL;DR: In this paper, the authors considered a special case of the first-order moving average additive Gaussian noise channel and showed that the feedback capacity of this channel is CFB=-log x0 where x0 is the unique positive root of the equation rhox2=(1-x2)(1-|alpha|x)2 and rho is the ratio of the average input power per transmission to the variance of the noise innovation Ui.
Proceedings ArticleDOI
Feedback Capacity of Stationary Gaussian Channels
TL;DR: This result shows that the celebrated Schalkwijk?Kailath coding scheme achieves the feedback capacity for the first-order autoregressive moving-average Gaussian channel, positively answering a long-standing open problem studied by Butman, SchalkWijk?Tiernan, Wolfowitz, Ozarow, Ordentlich, Yang?Kav?i??Tatikonda, and others.
References
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Journal ArticleDOI
A mathematical theory of communication
TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
Book
Coding Theorems of Information Theory
TL;DR: This chapter discusses the Discrete Memoryless Channel, a discrete memoryless channel with additive Gaussian noise, and the coding theorem, which states that message sequences on the periphery of the sphere or within a shell adjacent to the boundary should be considered to be discrete.
Journal ArticleDOI
A coding scheme for additive noise channels with feedback--II: Band-limited signals
TL;DR: This paper extends the scheme for effectively exploiting a noiseless feedback link associated with an additive white Gaussian noise channel to a band-limited channel with signal bandwidth restricted to (- W, W) and achieves the well-known channel capacity.
Journal ArticleDOI
A general formulation of linear feedback communication systems with solutions
TL;DR: The feedback coding problem for additive noise systems, in which the noise may be colored, nonstationary, and correlated between channels, is formulated in terms of arbitrary linear operations at the transmitting and receiving points, providing a unified approach for deriving new results.
Journal ArticleDOI
An upper bound to the capacity of the band-limited Gaussian autoregressive channel with noiseless feedback
J. Tiernan,J. Schalkwijk +1 more
TL;DR: Upper bounds to the capacity of band-limited Gaussian m th-order autoregressive channels with feedback and average energy constraint E are derived and are the tightest known for the first-order case.
Related Papers (5)
A coding scheme for additive noise channels with feedback--I: No bandwidth constraint
J. Schalkwijk,Thomas Kailath +1 more