On the Duality Between Slepian–Wolf Coding and Channel Coding Under Mismatched Decoding

Chen, Jun; He, Dongxiao; Jagmohan, A.

doi:10.1109/tit.2009.2025527

Cited by 23 publications

(29 citation statements)

References 12 publications

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“…This is the main intuition underlying the proof of the following theorem. A similar argument has been used in the context of variable-rate Slepian-Wolf coding under mismatched decoding [16].…”

Section: Variable-rate Slepian-wolf Coding: Above the Slepian-wolf Limitmentioning

confidence: 86%

On the Reliability Function of Variable-Rate Slepian-Wolf Coding

Chen

Jagmohan

et al. 2017

Entropy

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Abstract:The reliability function of variable-rate Slepian-Wolf coding is linked to the reliability function of channel coding with constant composition codes, through which computable lower and upper bounds are derived. The bounds coincide at rates close to the Slepian-Wolf limit, yielding a complete characterization of the reliability function in that rate region. It is shown that variable-rate Slepian-Wolf codes can significantly outperform fixed-rate Slepian-Wolf codes in terms of rate-error tradeoff. Variable-rate Slepian-Wolf coding with rate below the Slepian-Wolf limit is also analyzed. In sharp contrast with fixed-rate Slepian-Wolf codes for which the correct decoding probability decays to zero exponentially fast if the rate is below the Slepian-Wolf limit, the correct decoding probability of variable-rate Slepian-Wolf codes can be bounded away from zero.

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Section: Variable-rate Slepian-wolf Coding: Above the Slepian-wolf Limitmentioning

confidence: 86%

On the Reliability Function of Variable-Rate Slepian-Wolf Coding

Chen

Jagmohan

et al. 2017

Entropy

Self Cite

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“…Any other feasible rate function ρ(Q X ) will satisfy ρ(Q X ) ≥ ρ * (Q X , E e ) for some Q X ∈ P n (X ), and will consequently have inferior excessrate exponent, no matter what is the target rate. In other words, the excess-rate exponent is maximized by ρ * (Q X , E e ) simultaneously for every R. 8 Specifically, the average rate and the maximal rate (which is required to avoid buffer overflow) are determined. Indeed, for a given E e , the average rate is determined to be…”

Section: Excess-rate Performancementioning

confidence: 99%

“…In all the above papers, fixed-rate coding was assumed, perhaps because, as is well known, Slepian-Wolf (SW) coding is, in some sense, analogous to channel coding (without feedback) [2], [5], [8], [14], for which variable-rate is usually of no use. More recently, it was recognized that variable-rate SW coding may have improved performance.…”

mentioning

confidence: 99%

Optimum Tradeoffs Between the Error Exponent and the Excess-Rate Exponent of Variable-Rate Slepian–Wolf Coding

Weinberger

Merhav

2015

IEEE Trans. Inform. Theory

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We analyze the optimal tradeoff between the error exponent and the excess-rate exponent for variable-rate Slepian-Wolf codes. In particular, we first derive upper (converse) bounds on the optimal error and excess-rate exponents, and then lower (achievable) bounds, via a simple class of variable-rate codes which assign the same rate to all source blocks of the same type class. Then, using the exponent bounds, we derive bounds on the optimal rate functions, namely, the minimal rate assigned to each type class, needed in order to achieve a given target error exponent. The resulting excess-rate exponent is then evaluated. Iterative algorithms are provided for the computation of both bounds on the optimal rate functions and their excess-rate exponents. The resulting Slepian-Wolf codes bridge between the two extremes of fixed-rate coding, which has minimal error exponent and maximal excess-rate exponent, and averagerate coding, which has maximal error exponent and minimal excess-rate exponent.

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“…The case when the communicating parties assume different prior distributions while communicating a source from one party to another has recently been considered for the non-interactive setting. In [24], communicating a source with vanishing error is considered in the presence of side information when the joint probability distributions assumed at the encoder and the decoder are different. Reference [25] has incorporated shared randomness to facilitate compression when the source distribution assumed by the two parties are different from each other.…”

Section: Introductionmentioning

confidence: 99%

Two-Party Zero-Error Function Computation with Asymmetric Priors

Güler

Yener

Basu

et al. 2017

Entropy

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Abstract:We consider a two party network where each party wishes to compute a function of two correlated sources. Each source is observed by one of the parties. The true joint distribution of the sources is known to one party. The other party, on the other hand, assumes a distribution for which the set of source pairs that have a positive probability is only a subset of those that may appear in the true distribution. In that sense, this party has only partial information about the true distribution from which the sources are generated. We study the impact of this asymmetry on the worst-case message length for zero-error function computation, by identifying the conditions under which reconciling the missing information prior to communication is better than not reconciling it but instead using an interactive protocol that ensures zero-error communication without reconciliation. Accordingly, we provide upper and lower bounds on the minimum worst-case message length for the communication strategies with and without reconciliation. Through specializing the proposed model to certain distribution classes, we show that partially reconciling the true distribution by allowing a certain degree of ambiguity can perform better than the strategies with perfect reconciliation as well as strategies that do not start with an explicit reconciliation step. As such, our results demonstrate a tradeoff between the reconciliation and communication rates, and that the worst-case message length is a result of the interplay between the two factors.

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On the Duality Between Slepian–Wolf Coding and Channel Coding Under Mismatched Decoding

Cited by 23 publications

References 12 publications

On the Reliability Function of Variable-Rate Slepian-Wolf Coding

On the Reliability Function of Variable-Rate Slepian-Wolf Coding

Optimum Tradeoffs Between the Error Exponent and the Excess-Rate Exponent of Variable-Rate Slepian–Wolf Coding

Two-Party Zero-Error Function Computation with Asymmetric Priors

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