Channel Simulation via Interactive Communications

Yassaee, Mohammad Hossein; Gohari, Amin; Aref, Mohammad Reza

doi:10.1109/tit.2015.2428236

Cited by 39 publications

(10 citation statements)

References 19 publications

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“…Common information: The common information for this distribution was calculated in [2] and can be obtained from the above region. Let represent the value of rounded up to the nearest even number (20) Notice that this increases to 2 bits as the alphabet size increases.…”

Section: Withmentioning

confidence: 99%

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Distributed Channel Synthesis

Cuff

2013

IEEE Trans. Inform. Theory

202

341

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Two familiar notions of correlation are rediscovered as the extreme operating points for distributed synthesis of a discrete memoryless channel, in which a stochastic channel output is generated based on a compressed description of the channel input. Wyner's common information is the minimum description rate needed. However, when common randomness independent of the input is available, the necessary description rate reduces to Shannon's mutual information. This work characterizes the optimal trade-off between the amount of common randomness used and the required rate of description. We also include a number of related derivations, including the effect of limited local randomness, rate requirements for secrecy, applications to game theory, and new insights into common information duality. Our proof makes use of a soft covering lemma, known in the literature for its role in quantifying the resolvability of a channel. The direct proof (achievability) constructs a feasible joint distribution over all parts of the system using a soft covering, from which the behavior of the encoder and decoder is inferred, with no explicit reference to joint typicality or binning. Of auxiliary interest, this work also generalizes and strengthens this soft covering tool.Comment: To appear in IEEE Trans. on Information Theory (submitted Aug., 2012, accepted July, 2013), 26 pages, using IEEEtran.cl

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Section: Withmentioning

confidence: 99%

“…Other extensions to this problem can be found in the recent literature (e.g., [18], [20]- [22]), building on our introduction of the problem in [1], [23], and [2].…”

Section: Introductionmentioning

confidence: 99%

Distributed Channel Synthesis

Cuff

2013

IEEE Trans. Inform. Theory

202

341

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“…We want to understand the fundamental trade-offs between these rates to make this task possible. This problem was completely solved by Yassaee, Gohari, and Aref in [11]. However, this work does not address the problem of computing the simulation capacity R * for the setup in Fig.…”

Section: Figmentioning

confidence: 99%

“…As explained above, for (10), we may restrict to functions taking values in R ≥0 . However, in (11), the functions must take non-negative values. This is conventional and necessary in various"reverse" inequalities such as the reverse Minkowski and reverse Hölder inequalities.…”

Section: B Hypercontractivitymentioning

confidence: 99%

On Non-Interactive Simulation of Joint Distributions

Kamath

Anantharam

2016

IEEE Trans. Inform. Theory

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We consider the following non-interactive simulation problem: Alice and Bob observe sequences X n and Y n , respectively, where {(X i , Y i )} n i=1 are drawn independent identically distributed from P(x, y), and they output U and V , respectively, which is required to have a joint law that is close in total variation to a specified Q(u, v). It is known that the maximal correlation of U and V must necessarily be no bigger than that of X and Y if this is to be possible. Our main contribution is to bring hypercontractivity to bear as a tool on this problem. In particular, we show that if P(x, y) is the doubly symmetric binary source, then hypercontractivity provides stronger impossibility results than maximal correlation. Finally, we extend these tools to provide impossibility results for the k-agent version of this problem.

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“…For example, [2] considers a distributed multi-agent control problem, in which each agent generates actions based on its own observations of a source of randomness. The generation of dependent random variables in networks under a strong coordination constraint is considered in [3], [4] by investigating bidirectional transmissions in several rounds; however, these works only address the coordination of two nodes. An extension of the ideas to a two-way relay channel and strong coordination can be found in [5].…”

Section: Introductionmentioning

confidence: 99%

Strong coordination over a three-terminal relay network

Bloch

Kliewer

2014

2014 IEEE Information Theory Workshop (ITW 2014)

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We study the problem of strong coordination in a three-terminal relay network, in which agents communicate to ensure that their actions follow a joint behavior specified by a prescribed joint distribution of actions. The model unifies several coordination schemes, including line and broadcast coordination. We derive several inner bounds to the strong capacity region; in particular, we prove the achievability of a subset of coordination rate-tuples, which provides insight into the relative performance of line, broadcast, and relay coordination.• Upon observing m 0 and x n

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Channel Simulation via Interactive Communications

Cited by 39 publications

References 19 publications

Distributed Channel Synthesis

Distributed Channel Synthesis

On Non-Interactive Simulation of Joint Distributions

Strong coordination over a three-terminal relay network

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