Broadcast Channels and Input-Cost Side Information

Haim, Eli; Zamir, Ram

doi:10.1109/eeei.2006.321127

Cited by 2 publications

(3 citation statements)

References 12 publications

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“…One is their role in the "dual" problem of source coding with side information (the Wyner-Ziv problem), which we briefly investigated in the last section. We can also use DSN to construct examples of large capacity loss in the broadcast setting, e.g., for the loss of sum-rate due to lack of cooperation between the receivers (for background, see [12]). Another use of difference sets is in constructing expander graphs.…”

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confidence: 99%

Entropy Amplification Property and the Loss for Writing on Dirty Paper

Cohen

Zamir

2008

IEEE Trans. Inform. Theory

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Costa's celebrated "writing on dirty paper" (WDP) shows that the powerconstrained channel Y = X + S + Z, with Gaussian Z, has the same capacity as the standard AWGN channel Y = X + Z, provided that the "interference" S (no matter how strong it is) is known at the transmitter. While this ability for perfect interference cancelation is very appealing, it relies heavily on the Gaussianity of the (unknown) noise Z. We construct an example of "bad" noise for writing on dirty paper, namely, "difference set noise". If the interference S is strong, then difference-set noise limits the WDP capacity to at most 2 bits. At the same time, like in the AWGN case, the zero-interference capacity grows without bound with the input constraint. Thus almost 100 % of the available capacity is lost in WDP in the presence of difference-set noise. This high capacity loss is due to the "entropy amplification property" (EAP) of noise with an aperiodic probability distribution. Using the EAP and the duality between WDP and Wyner-Ziv source coding, we also give an example of dramatic rate-loss in quantizing encrypted source.

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confidence: 99%

Entropy Amplification Property and the Loss for Writing on Dirty Paper

Cohen

Zamir

2008

IEEE Trans. Inform. Theory

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“…This constitute the "channel state" that constrains the encoding to the second terminal. Therefore R 2 = I(Y 2 ; U 2 )−I(U 1 ; U 2 ) which is similar to the expression in (1), where the information to the first terminal U 1 acts as S, the "side information" that constrains the encoding to the second terminal.…”

Section: B Broadcast Channelsmentioning

confidence: 70%

“…the channel is of the form Y = f (X, S), when S is given at the encoder. This fact can be seen by substituting U = Y in the C enc expression in (1).…”

Section: A Cost State Side Information Problemmentioning

confidence: 99%

On the tightness of Marton's regions for semi-additive broadcast channels

Haim

Zamir

2009

2009 IEEE International Symposium on Information Theory

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Abstract-We study cost constrained side-information channels, where the cost function depends on a state which is known only to the encoder. In the additive noise case, we bound the capacity loss due to not knowing the cost state at the decoder and show that it is small under various assumptions, and goes to zero in the limit of weak noise. This model plays an important role in the (non-degraded) broadcast channel. In the semi-additive noise case, we bound the gap between the best known single letter achievable region and the true capacity region, using tools developed for the first problem. In the limit of weak noise, we show that the bounds coincide, thus we get the complete characterization of the capacity region.

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Broadcast Channels and Input-Cost Side Information

Cited by 2 publications

References 12 publications

Entropy Amplification Property and the Loss for Writing on Dirty Paper

Entropy Amplification Property and the Loss for Writing on Dirty Paper

On the tightness of Marton's regions for semi-additive broadcast channels

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