Empirical Properties of Good Channel Codes

Qinghua Devon Ding, Sidharth Jaggi, Shashank Vatedka, Yihan Zhang

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)

Abstract

In this article, we revisit the classical problem of channel coding and obtain novel results on properties of capacity- achieving codes. Specifically, we give a linear algebraic characterization of the set of capacity-achieving input distributions for discrete memoryless channels. This allows us to characterize the dimension of the manifold on which the capacity-achieving distributions lie. We then proceed by examining empirical properties of capacity-achieving codebooks by showing that the joint-type of k-tuples of codewords in a good code must be close to the k- fold product of the capacity-achieving input distribution. While this conforms with the intuition that all capacity-achieving codes must behave like random capacity-achieving codes, we also show that some properties of random coding ensembles do not hold for all codes. We prove this by showing that there exist pairs of communication problems such that random code ensembles simultaneously attain capacities of both problems, but certain (superposition ensembles) do not.Due to lack of space, several proofs have been omitted but can be found at https://sites.google.com/view/yihan/

Original languageEnglish
Title of host publication2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2337-2342
Number of pages6
ISBN (Electronic)9781728164328, 9781728164311
ISBN (Print)9781728164335
DOIs
Publication statusPublished - 24 Aug 2020
Event2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States
Duration: 21 Jul 202026 Jul 2020

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2020-June
ISSN (Print)2157-8095

Conference

Conference2020 IEEE International Symposium on Information Theory, ISIT 2020
Country/TerritoryUnited States
CityLos Angeles
Period21/07/2026/07/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

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