Maximum Likelihood Decoding for Channels with Uniform Noise and Signal Dependent Offset

Renfei Bu, Jos H. Weber, Kees A. Schouhamer Immink

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

Maximum likelihood (ML) decision criteria have been developed for channels suffering from signal independent offset mismatch. Here, such criteria are considered for signal dependent offset, which means that the value of the offset may differ for distinct signal levels rather than being the same for all levels. An ML decision criterion is derived, assuming uniform distributions for both the noise and the offset. In particular, for the proposed ML decoder, bounds are determined on the standard deviations of the noise and the offset which lead to a word error rate equal to zero. Simulation results are presented confirming the findings.

Original languageEnglish
Title of host publication2020 IEEE International Symposium on Information Theory (ISIT)
Subtitle of host publicationProceedings
Place of PublicationPiscataway
PublisherIEEE
Pages706-710
Number of pages5
ISBN (Electronic)978-1-7281-6432-8
ISBN (Print)978-1-7281-6433-5
DOIs
Publication statusPublished - 2020
Event2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States
Duration: 21 Jul 202026 Jul 2020

Conference

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

Keywords

  • Maximum likelihood decoding
  • offset mismatch
  • signal dependent offset

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