Analysis of Iterated Hard Decision Decoding of Product Codes with Reed-Solomon Component Codes

Jørn Justesen; Tom Høholdt

doi:10.1109/ITW.2007.4313069

Analysis of Iterated Hard Decision Decoding of Product Codes with Reed-Solomon Component Codes

Jørn Justesen (Invited author), Tom Høholdt (Invited author)

Department of Photonics Engineering

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

Products of Reed-Solomon codes are important in applications because they offer a combination of large blocks, low decoding complexity, and good performance. A recent result on random graphs can be used to show that with high probability a large number of errors can be corrected by iterating minimum distance decoding. We present an analysis related to density evolution which gives the exact asymptotic value of the decoding threshold and also provides a closed form approximation to the distribution of errors in each step of the decoding of finite length codes.

Original language	English
Title of host publication	IEEE Information Theory Workshop, 2007. ITW '07.
Place of Publication	Lake Tahoe
Publisher	IEEE
Publication date	2007
ISBN (Print)	1-4244-1564-0
DOIs	https://doi.org/10.1109/ITW.2007.4313069
Publication status	Published - 2007
Event	IEEE Information Theory Workshop, 2007. - Tahoe City, CA Duration: 1 Jan 2007 → …

Conference

Conference	IEEE Information Theory Workshop, 2007.
City	Tahoe City, CA
Period	01/01/2007 → …

Bibliographical note

Copyright: 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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title = "Analysis of Iterated Hard Decision Decoding of Product Codes with Reed-Solomon Component Codes",

abstract = "Products of Reed-Solomon codes are important in applications because they offer a combination of large blocks, low decoding complexity, and good performance. A recent result on random graphs can be used to show that with high probability a large number of errors can be corrected by iterating minimum distance decoding. We present an analysis related to density evolution which gives the exact asymptotic value of the decoding threshold and also provides a closed form approximation to the distribution of errors in each step of the decoding of finite length codes.",

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