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Open Access

Iterative Decoding of Concatenated Codes: A Tutorial

EURASIP Journal on Advances in Signal Processing20052005:967375

https://doi.org/10.1155/ASP.2005.762

Received: 29 September 2003

Published: 15 May 2005

Abstract

The turbo decoding algorithm of a decade ago constituted a milestone in error-correction coding for digital communications, and has inspired extensions to generalized receiver topologies, including turbo equalization, turbo synchronization, and turbo CDMA, among others. Despite an accrued understanding of iterative decoding over the years, the "turbo principle" remains elusive to master analytically, thereby inciting interest from researchers outside the communications domain. In this spirit, we develop a tutorial presentation of iterative decoding for parallel and serial concatenated codes, in terms hopefully accessible to a broader audience. We motivate iterative decoding as a computationally tractable attempt to approach maximum-likelihood decoding, and characterize fixed points in terms of a "consensus" property between constituent decoders. We review how the decoding algorithm for both parallel and serial concatenated codes coincides with an alternating projection algorithm, which allows one to identify conditions under which the algorithm indeed converges to a maximum-likelihood solution, in terms of particular likelihood functions factoring into the product of their marginals. The presentation emphasizes a common framework applicable to both parallel and serial concatenated codes.

Keywords and phrases

iterative decodingmaximum-likelihood decodinginformation geometrybelief propagation

Authors’ Affiliations

(1)
Département Communications, Images et Traitement de l' Information, Institut National des Télécommunications, Evry Cedex, France
(2)
Department of Electrical Engineering and Computer Science, Catholic University of America, Washington, USA

Copyright

© Phillip A. Regalia 2005

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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