2013 IEEE International Symposium on Information Theory 2013
DOI: 10.1109/isit.2013.6620807
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Assisted sampling of correlated sources

Abstract: Abstract-We study a distributed sampling scenario in which two agents observing components of a correlated source must each generate components of a second correlated source. The agents are aided by an "omniscient" third terminal which observes the two input sources and transmits rate-limited messages to assist the terminals in generating the required correlation in their outputs. We identify two sub-cases of this problem based on how the generated sources must depend on the input sources.

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Cited by 3 publications
(5 citation statements)
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References 17 publications
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“…In the protocol we assumed that the terminals have access to shared randomness B [1:r] which is not present in the model (note that ω is the real common randomness shared between the two terminals in the model). To get rid of the shared randomness B [1:r] , we would like to condition on a particular instance of B on the LHS of (18). Thus, we want to impose certain constraints on the size of the bins to guarantee that the marginal pmfs do not change.…”
Section: Assign Uniformly and Independently Two Bin Indicesmentioning
confidence: 99%
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“…In the protocol we assumed that the terminals have access to shared randomness B [1:r] which is not present in the model (note that ω is the real common randomness shared between the two terminals in the model). To get rid of the shared randomness B [1:r] , we would like to condition on a particular instance of B on the LHS of (18). Thus, we want to impose certain constraints on the size of the bins to guarantee that the marginal pmfs do not change.…”
Section: Assign Uniformly and Independently Two Bin Indicesmentioning
confidence: 99%
“…] on the LHS of(18). Thus, we want to impose certain constraints on the size of the bins to guarantee that the marginal pmfs do not change.…”
mentioning
confidence: 99%
“…For the converse, suppose a rate triplet (R, R 1 , R 2 ) is achievable for q(x). Then, the existing converse arguments [8,Theorem 6], stated in a form suitable to our setting, gives us that there exists R ≥ 0 and jointly distributed random variables (Q, U, V ) such that U & V are independent and uniformly distributed over [1 : 2 R1 ] and [1 : 2 R2 ] respectively such that…”
Section: Proofsmentioning
confidence: 99%
“…The problem of generating correlated random variables via interactive communication has been studied by Gohari and Anantharm [6] and Yassaee et al [7]. Out previous work [8] studies a distributed sampling problem in which a genie can help two processors observing correlated sources to output correlated sequences.…”
Section: Introductionmentioning
confidence: 99%
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