NDLI: On Low-Complexity Maximum-Likelihood Decoding of Convolutional Codes

Content Provider	IEEE Xplore Digital Library
Author	Jie Luo
Copyright Year	1963
Abstract	This letter considers the average complexity of maximum-likelihood (ML) decoding of convolutional codes. ML decoding can be modeled as finding the most probable path taken through a Markov graph. Integrated with the Viterbi algorithm (VA), complexity reduction methods often use the sum log likelihood (SLL) of a Markov path as a bound to disprove the optimality of other Markov path sets and to consequently avoid exhaustive path search. In this letter, it is shown that SLL-based optimality tests are inefficient if one fixes the coding memory and takes the codeword length to infinity. Alternatively, optimality of a source symbol at a given time index can be testified using bounds derived from log likelihoods of the neighboring symbols. It is demonstrated that such neighboring log likelihood (NLL)-based optimality tests, whose efficiency does not depend on the codeword length, can bring significant complexity reduction. The results are generalized to ML sequence detection in a class of discrete-time hidden Markov systems.
Sponsorship	IEEE Information Theory Society
Starting Page	5756
Ending Page	5760
Page Count	5
File Size	233428
File Format	PDF
ISSN	00189448
Volume Number	54
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Maximum likelihood decoding Convolutional codes Testing Iterative decoding Maximum likelihood detection Viterbi algorithm H infinity control Hidden Markov models Lattices Upper bound Viterbi algorithm (VA) Coding complexity convolutional code hidden Markov model maximum-likelihood (ML) decoding
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems Computer Science Applications

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