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

Weinberger, Nir; Merhav, Neri

doi:10.1109/tit.2015.2405537

Cited by 24 publications

(24 citation statements)

References 34 publications

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“…It is shown that variable-rate Slepian-Wolf coding can outperform fixed-rate Slepian-Wolf coding in terms of rate-error tradeoff. Finally, we would like to mention that Theorem 1 has been generalized by Weinberger and Merhav in their recent paper on the optimal tradeoff between the error exponent and the excess-rate exponent of variable-rate Slepian-Wolf coding [19]. …”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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

Chen

Jagmohan

et al. 2017

Entropy

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“…Obviously, if V ′ is degenerate (e.g., equal to a fixed v ∈ V with probability one), then we are back to (11). For another extreme case, if the channel is clean, Q is uniform, and the channel alphabet is very large, thenÎ(X; Y ′ ) =Ĥ(X) = log |X | is large as well, and then R ∧Î(X ′ ; Y ) is dominated by R. In this case, we recover the SW random binning error exponent (see, e.g., [32] and references therein).…”

Section: Both Achieve E(r Q)mentioning

confidence: 84%

“…At the same time, considering the analogy between channel coding and Slepian-Wolf (SW) source coding, it is not surprising that universal schemes for SW decoding, like the minimum entropy (ME) decoder, have also been derived, first, by Csiszár and Körner [4, Exercise 3.1.6], and later further developed by others in various directions, see, e.g., [1], [6], [12], [27], [28], [32].…”

Section: Introductionmentioning

confidence: 99%

Universal decoding for source–channel coding with side information

Merhav

2016

Communications in Information and Systems

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We consider a setting of Slepian-Wolf coding, where the random bin of the source vector undergoes channel coding, and then decoded at the receiver, based on additional side information, correlated to the source. For a given distribution of the randomly selected channel codewords, we propose a universal decoder that depends on the statistics of neither the correlated sources nor the channel, assuming first that they are both memoryless. Exact analysis of the random-binning/randomcoding error exponent of this universal decoder shows that it is the same as the one achieved by the optimal maximum a-posteriori (MAP) decoder. Previously known results on universal Slepian-Wolf source decoding, universal channel decoding, and universal source-channel decoding, are all obtained as special cases of this result. Subsequently, we further generalize the results in several directions, including: (i) finite-state sources and finite-state channels, along with a universal decoding metric that is based on Lempel-Ziv parsing, (ii) arbitrary sources and channels, where the universal decoding is with respect to a given class of decoding metrics, and (iii) full (symmetric) Slepian-Wolf coding, where both source streams are separately fed into random-binning source encoders, followed by random channel encoders, which are then jointly decoded by a universal decoder.

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“…It appears then that correct estimation of S is essentially equivalent to correct estimation of X, as in ordinary Slepian-Wolf decoding [6] (see also [15] and references therein), where there is no secret key at all (or alternatively, R s → ∞). Indeed, the Slepian-Wolf coding component of the joint source-channel coding system, analyzed in [10, Section IV] under the generalized likelihood decoder, contributes the very same error exponent as asserted in Theorem 1.…”

Section: False-reject Error Analysismentioning

confidence: 99%

Ensemble Performance of Biometric Authentication Systems Based on Secret Key Generation

Merhav

2019

IEEE Trans. Inform. Theory

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We study the ensemble performance of biometric authentication systems, based on secret key generation, which work as follows. In the enrollment stage, an individual provides a biometric signal that is mapped into a secret key and a helper message, the former being prepared to become available to the system at a later time (for authentication), and the latter is stored in a public database. When an authorized user requests authentication, claiming his/her identity as one of the subscribers, s/he has to provide a biometric signal again, and then the system, which retrieves also the helper message of the claimed subscriber, produces an estimate of the secret key, that is finally compared to the secret key of the claimed user. In case of a match, the authentication request is approved, otherwise, it is rejected. Referring to an ensemble of systems based on Slepian-Wolf binning, we provide a detailed analysis of the false-reject and false-accept probabilities, for a wide class of stochastic decoders. We also comment on the security for the typical code in the ensemble.

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

Cited by 24 publications

References 34 publications

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

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

Universal decoding for source–channel coding with side information

Ensemble Performance of Biometric Authentication Systems Based on Secret Key Generation

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