Simplified erasure/list decoding

Nir Weinberger, Neri Merhav

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We consider the problem of erasure/list decoding using certain classes of simplified decoders. Specifically, we assume a class of erasure/list decoders, such that a codeword is in the list if its likelihood is larger than a threshold. This class of decoders both approximates the optimal decoder of Forney, and also includes the following simplified subclasses of decoding rules: The first is a function of the output vector only, but not the codebook (which is most suitable for high rates), and the second is a scaled version of the maximum likelihood decoder (which is most suitable for low rates). We provide singleletter expressions for the exact random coding exponents of any decoder in these classes, operating over a discrete memoryless channel. For each class of decoders, we find the optimal decoder within the class, in the sense that it maximizes the erasure/list exponent, under a given constraint on the error exponent. We establish the optimality of the simplified decoders of the first and second kind for low and high rates, respectively.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781467377041
StatePublished - 28 Sep 2015
Externally publishedYes
EventIEEE International Symposium on Information Theory, ISIT 2015 - Hong Kong, Hong Kong
Duration: 14 Jun 201519 Jun 2015

Publication series

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


ConferenceIEEE International Symposium on Information Theory, ISIT 2015
Country/TerritoryHong Kong
CityHong Kong


  • Erasure/list decoding
  • decoding complexity
  • error exponents
  • mismatch decoding
  • random coding


Dive into the research topics of 'Simplified erasure/list decoding'. Together they form a unique fingerprint.

Cite this