Analysis and design of raptor codes for joint decoding using Information Content evolution

Analysis and design of raptor codes for joint decoding using Information Content evolution

In this paper, we present an analytical analysis of the convergence of raptor codes under joint decoding over the binary input additive white noise channel (BIAWGNC), and derive an optimization method. We use Information Content evolution under Gaussian approximation, and focus on a new decoding sch...

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Bibliographic Details
Main Authors: Venkiah, Auguste, Poulliat, Charly, Declercq, David
Format: Journal Article
Language:English
Published: 17-01-2007
Subjects:
Computer Science - Information Theory
Mathematics - Information Theory
Online Access:Get full text
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Summary:In this paper, we present an analytical analysis of the convergence of raptor codes under joint decoding over the binary input additive white noise channel (BIAWGNC), and derive an optimization method. We use Information Content evolution under Gaussian approximation, and focus on a new decoding scheme that proves to be more efficient: the joint decoding of the two code components of the raptor code. In our general model, the classical tandem decoding scheme appears to be a subcase, and thus, the design of LT codes is also possible.
DOI:10.48550/arxiv.cs/0701103