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Article: Feedback Capacity of the Continuous-Time ARMA(1,1) Gaussian Channel

TitleFeedback Capacity of the Continuous-Time ARMA(1,1) Gaussian Channel
Authors
KeywordsAdditives
AWGN channels
Channel capacity
Channel capacity
colored noise
continuous-time systems
Encoding
feedback
Feedback amplifiers
Gaussian channels
Gaussian processes
Random variables
Issue Date17-Jun-2024
PublisherInstitute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Information Theory, 2024, v. 70, n. 9, p. 6171-6188 How to Cite?
AbstractWe consider the continuous-time ARMA(1,1) Gaussian channel and derive its feedback capacity in closed form. More specifically, the channel is given by y(t) = x(t) + z(t), where the channel input {x(t)} satisfies average power constraint P and the noise {z(t)} is a first-order autoregressive moving average (ARMA(1,1)) Gaussian process satisfying z’(t)+κ z(t) = (κ + λ) w(t)+w’(t) where κ > 0, λ ϵ R and }w(t)} is a white Gaussian process with unit double-sided spectral density. We show that the feedback capacity of this channel is equal to the unique positive root of the equation P(x + κ)2 = 2x(x + |κ + λ|)2 when -2κ < λ < 0 and is equal to P/2 otherwise. Among many others, this result shows that, as opposed to a discrete-time additive Gaussian channel, feedback may not increase the capacity of a continuous-time additive Gaussian channel even if the noise process is colored. The formula enables us to conduct a thorough analysis of the effect of feedback on the capacity for such a channel. We characterize when the feedback capacity equals or doubles the non-feedback capacity; moreover, we disprove continuous-time analogues of the half-bit bound and Cover’s 2P conjecture for discrete-time additive Gaussian channels.
Persistent Identifierhttp://hdl.handle.net/10722/347774
ISSN
2023 Impact Factor: 2.2
2023 SCImago Journal Rankings: 1.607

 

DC FieldValueLanguage
dc.contributor.authorSu, Jun-
dc.contributor.authorHan, Guangyue-
dc.contributor.authorShamai, Shlomo-
dc.date.accessioned2024-09-28T00:30:28Z-
dc.date.available2024-09-28T00:30:28Z-
dc.date.issued2024-06-17-
dc.identifier.citationIEEE Transactions on Information Theory, 2024, v. 70, n. 9, p. 6171-6188-
dc.identifier.issn0018-9448-
dc.identifier.urihttp://hdl.handle.net/10722/347774-
dc.description.abstractWe consider the continuous-time ARMA(1,1) Gaussian channel and derive its feedback capacity in closed form. More specifically, the channel is given by y(t) = x(t) + z(t), where the channel input {x(t)} satisfies average power constraint P and the noise {z(t)} is a first-order autoregressive moving average (ARMA(1,1)) Gaussian process satisfying z’(t)+κ z(t) = (κ + λ) w(t)+w’(t) where κ > 0, λ ϵ R and }w(t)} is a white Gaussian process with unit double-sided spectral density. We show that the feedback capacity of this channel is equal to the unique positive root of the equation P(x + κ)<sup>2</sup> = 2x(x + |κ + λ|)<sup>2</sup> when -2κ < λ < 0 and is equal to P/2 otherwise. Among many others, this result shows that, as opposed to a discrete-time additive Gaussian channel, feedback may not increase the capacity of a continuous-time additive Gaussian channel even if the noise process is colored. The formula enables us to conduct a thorough analysis of the effect of feedback on the capacity for such a channel. We characterize when the feedback capacity equals or doubles the non-feedback capacity; moreover, we disprove continuous-time analogues of the half-bit bound and Cover’s 2P conjecture for discrete-time additive Gaussian channels.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Transactions on Information Theory-
dc.subjectAdditives-
dc.subjectAWGN channels-
dc.subjectChannel capacity-
dc.subjectChannel capacity-
dc.subjectcolored noise-
dc.subjectcontinuous-time systems-
dc.subjectEncoding-
dc.subjectfeedback-
dc.subjectFeedback amplifiers-
dc.subjectGaussian channels-
dc.subjectGaussian processes-
dc.subjectRandom variables-
dc.titleFeedback Capacity of the Continuous-Time ARMA(1,1) Gaussian Channel-
dc.typeArticle-
dc.identifier.doi10.1109/TIT.2024.3415736-
dc.identifier.scopuseid_2-s2.0-85196550300-
dc.identifier.volume70-
dc.identifier.issue9-
dc.identifier.spage6171-
dc.identifier.epage6188-
dc.identifier.eissn1557-9654-
dc.identifier.issnl0018-9448-

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