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Sakai, Yuta; Tan, Vincent Y. F.
IEEE transactions on information theory, 2022-March, 2022-3-00, Volume: 68, Issue: 3Journal Article
This study considers the unconditional smooth Rényi entropy proposed by Renner and Wolf ASIACRYPT, 2005, the smooth conditional Rényi entropy proposed by Kuzuoka IEEE Trans. Inf. Th., 66(3), 1674-1690, 2020, and a novel quantity which we term the conditional smooth -⋆ entropy. The latter two quantities can be specialized to the first in the absence of side-information. We explore the operational roles of these smooth Rényi entropies by establishing one-shot coding theorems for several information-theoretic problems, including Campbell's source coding problem, the Arıkan-Massey guessing problem, and the Bunte-Lapidoth task encoding problem. We consider these problems in cases where the errors are non-vanishing and for each problem, we consider two error formalisms: the average and maximum error criteria, where the averaging and maximization are taken with respect to the side-information. Using the one-shot coding theorems, we conclude that Kuzuoka's smooth conditional Rényi entropy and the conditional smooth-⋆ entropy are the solutions to the problems involving the average and maximum error criteria, respectively. Furthermore, we examine asymptotic expansions of these entropies when the underlying source with its side-information is stationary and memoryless. Applying our asymptotic expansions to the one-shot coding theorems, we derive various fundamental limits for these problems. We show that, under non-degenerate settings, the first-order fundamental limits differ under the average and maximum error criteria. This is in contrast to a different but related setting considered by the present authors IEEE Trans. Inf. Th., 66(12), 7565-7587, 2020, for variable-length conditional source coding allowing errors, in which the first-order terms are identical but the second-order terms are different under these error criteria.
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