An achievable region and outer bound for the Gaussian broadcast channel with feedback (Corresp.)

Ozarow, L.H.; Leung-Yan-Cheong, S.

doi:10.1109/tit.1984.1056923

Cited by 149 publications

(248 citation statements)

References 9 publications

Supporting

Mentioning

245

Contrasting

Order By: Relevance

“…We note, however, that while Marton's upper bound is the strongest, it is valid only for the two-receiver case, while Sato's bound and the DSM bound can be extended to more than two receivers. The effect of feedback on the capacity of the Gaussian broadcast channel was studied in [18] and [19], and in [20] the case of correlated sources was considered. A survey on the topic, with extensive references to previous work, can be found in [21].…”

Section: B the Discrete Memoryless Broadcast Channel (Dmbc)mentioning

confidence: 99%

Broadcast Channels With Cooperating Decoders

Dabora

Servetto

2006

IEEE Trans. Inform. Theory

145

163

View full text Add to dashboard Cite

Abstract-We consider the problem of communicating over the general discrete memoryless broadcast channel (BC) with partially cooperating receivers. In our setup, receivers are able to exchange messages over noiseless conference links of finite capacities, prior to decoding the messages sent from the transmitter. In this paper we formulate the general problem of broadcast with cooperation. We first find the capacity region for the case where the BC is physically degraded. Then, we give achievability results for the general broadcast channel, for both the two independent messages case and the single common message case.Index Terms-Broadcast channels, cooperative broadcast, relay channels, channel capacity, network information theory.

show abstract

Section: B the Discrete Memoryless Broadcast Channel (Dmbc)mentioning

confidence: 99%

Broadcast Channels With Cooperating Decoders

Dabora

Servetto

2006

IEEE Trans. Inform. Theory

145

163

View full text Add to dashboard Cite

show abstract

“…And it has been shown by El Gamal [18] that feedback cannot increase the capacity region of a physically degraded Gaussian BC. On the other hand, it was shown by Ozarow and Leung [57] that feedback can increase the capacity of stochastically degraded Gaussian BCs, whose capacity regions are far less understood.…”

Section: Continuous-time Multi-user Gaussian Channelsmentioning

confidence: 99%

“…Employing the approximation approach, we use a modified argument in [18] to show that feedback does not increase the capacity region of a physically degraded continuous-time Gaussian BC, and on the other hand, a translated version of the argument in [57] to show that feedback does increase the capacity region of certain continuous-time Gaussian BC.…”

Section: Continuous-time Multi-user Gaussian Channelsmentioning

confidence: 99%

“…We only provide the sketch of the proof, since it is largely based on a translated version of the argument in [57].…”

Section: Gaussian Bcsmentioning

confidence: 99%

“…With feedback, one can use the following variation [57] of the Schalkwijk-Kailath coding scheme [63] over [0, T ] at discrete time points {t n,i } that form an equidistant ∆ n of stepsize δ n : For the channel input, after some proper initialization, at time t ∈ [t n,i , t n,i+1 ), we send…”

Section: Gaussian Bcsmentioning

confidence: 99%

See 2 more Smart Citations

Continuous Time Gaussian Channels

Ihara¹

1993

Information Theory for Continuous Systems

View full text Add to dashboard Cite

A continuous-time white Gaussian channel can be formulated using a white Gaussian noise, and a conventional way for examining such a channel is the sampling approach based on the Shannon-Nyquist sampling theorem, where the original continuoustime channel is converted to an equivalent discrete-time channel, to which a great variety of established tools and methodology can be applied. However, one of the key issues of this scheme is that continuous-time feedback and memory cannot be incorporated into the channel model. It turns out that this issue can be circumvented by considering the Brownian motion formulation of a continuous-time white Gaussian channel. Nevertheless, as opposed to the white Gaussian noise formulation, a link that establishes the information-theoretic connection between a continuous-time channel under the Brownian motion formulation and its discrete-time counterparts has long been missing. This paper is to fill this gap by establishing causality-preserving connections between continuous-time Gaussian feedback/memory channels and their associated discrete-time versions in the forms of sampling and approximation theorems, which we believe will play important roles in the long run for further developing continuous-time information theory.As an immediate application of the approximation theorem, we propose the socalled approximation approach to examine continuous-time white Gaussian channels in the point-to-point or multi-user setting. It turns out that the approximation approach, complemented by relevant tools from stochastic calculus, can enhance our understanding of continuous-time Gaussian channels in terms of giving alternative and strengthened interpretation to some long-held folklore, recovering "long known" results from new perspectives, and rigorously establishing new results predicted by the intuition that the approximation approach carries. More specifically, using the approximation approach complemented by relevant tools from stochastic calculus, we first derive the capacity regions of continuous-time white Gaussian multiple access channels and broadcast channels, and we then analyze how feedback affects their capacity regions: feedback will increase the capacity regions of some continuous-time white Gaussian broadcast channels, while it will not increase capacity regions of continuous-time white Gaussian multiple access channels.

show abstract