Network compression: Worst-case analysis

Himanshu Asnani, Ilan Shomorony, A. Salman Avestimehr, Tsachy Weissman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider the problem of communicating a distributed correlated memoryless source over a memoryless network, from source nodes to destination nodes, under quadratic distortion constraints. We show the following two complementary results: (a) for an arbitrary memoryless network, among all distributed memoryless sources with a particular correlation, Gaussian sources are the worst compressible, that is, they admit the smallest set of achievable distortion tuples, and (b) for any arbitrarily distributed memoryless source to be communicated over a memoryless additive noise network, among all noise processes with a fixed correlation, Gaussian noise admits the smallest achievable set of distortion tuples. In each case, given a coding scheme for the corresponding Gaussian problem, we provide a technique for the construction of a new coding scheme that achieves the same distortion at the destination nodes in a non-Gaussian scenario with the same correlation structure.

Original languageEnglish (US)
Title of host publication2013 IEEE International Symposium on Information Theory, ISIT 2013
Pages196-200
Number of pages5
DOIs
StatePublished - Dec 19 2013
Externally publishedYes
Event2013 IEEE International Symposium on Information Theory, ISIT 2013 - Istanbul, Turkey
Duration: Jul 7 2013Jul 12 2013

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095

Other

Other2013 IEEE International Symposium on Information Theory, ISIT 2013
CountryTurkey
CityIstanbul
Period7/7/137/12/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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  • Cite this

    Asnani, H., Shomorony, I., Avestimehr, A. S., & Weissman, T. (2013). Network compression: Worst-case analysis. In 2013 IEEE International Symposium on Information Theory, ISIT 2013 (pp. 196-200). [6620215] (IEEE International Symposium on Information Theory - Proceedings). https://doi.org/10.1109/ISIT.2013.6620215