Gray-Wyner and Slepian-Wolf Guessing

Graczyk, Robert; Lapidoth, Amos

doi:10.1109/isit44484.2020.9174298

Cited by 8 publications

(3 citation statements)

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“…Gray-Wyner coding is considered part of the field of Distributed Source Coding (DSC), where multiple independent pieces of information can be compressed at once, and the goal is reduce bandwidth usage by limiting communication between users. SW coding falls under DSC and is similar to Gray-Wyner coding, with changes in the general structure [12]. In fact, Merikhi and Soleymani use the idea of decoding using side information (a feature of SW coding) in their CC implementation, although it is only used to compress two information sources [7], [8].…”

Section: A Background and Motivationsmentioning

confidence: 99%

Greedy Iterative and Meta-Heuristic Clustering With Coded Caching and Slepian-Wolf Compression for Correlated Content

Rosen,

Cheng

2024

IEEE Access

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Content caching has emerged as an effective approach to combat the increasing strains on our current network infrastructure. This method is further improved when combining caching with source coding. However, additional complexity is incurred by creating this hybrid method, as the source coding component comes with associated feasibility constraints and decoding costs. This paper presents an approach to balance this complexity with the coding gains by selecting the best-performing subset of files to compress, while the others are left uncoded. This problem is shown to be NP-hard in general and difficult to solve in an iteration-free manner. To this end, two novel approaches are outlined: an iterative-based solution, which uses the features of the entropy function to derive the most suitable files to compress jointly, and a metaheuristic version, which is based on the Genetic Algorithm. When compared to an exhaustive search, the proposed solutions are found to be sub-optimal but falling above the 90th percentile of all possible solutions on average. Significantly, the iterative method produces results within one percentile of the meta-heuristic approach yet it finds a solution 2.31 times faster. The iterative approach has an additional benefit, in that it is able to predict the relative gains when adding more files to a compression group. It is thus able to terminate prematurely if the estimated gains are less than a chosen threshold.

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Section: A Background and Motivationsmentioning

confidence: 99%

Greedy Iterative and Meta-Heuristic Clustering With Coded Caching and Slepian-Wolf Compression for Correlated Content

Rosen,

Cheng

2024

IEEE Access

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“…In this section, we formally define and state some known results on the Rényi common information. The following definition, which differs from an alternative one in Graczyk and Lapidoth [66], mirrors that of the Wyner's common information from the perspective of the distributed simulation problem (Definition 2.1.2). In the following, we only consider s ∈ (−1, 1] ∪ {∞}.…”

Section: Rényi Common Informationmentioning

confidence: 99%

Common Information, Noise Stability, and Their Extensions

Tan

2022

FNT in Communications and Information Theory

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“…Numerous other variations on the Massey-Arıkan guessing problem were studied over the years. Sundaresan [12] derived an expression for the smallest guessing moment when the source distribution is only partially known to the guesser; in [13], [14], the authors constructed and analyzed optimal decentralized guessing strategies (for multiple guessers that cannot communicate); in [15], Weinberger and Shayevitz quantified the value of a single bit of side-information provided to the guesser prior to guessing; in [16], the authors studied the guessing problem using an information-geometric approach; and in [17] and [11] the authors studied the distributed guessing problem on Gray-Wyner and Stelpian-Wolf networks.…”

Section: Introductionmentioning

confidence: 99%