A reduced-complexity online state sequence and parameter estimator for superimposed convolutional coded signals

TitleA reduced-complexity online state sequence and parameter estimator for superimposed convolutional coded signals
Publication TypeJournal Article
Year of Publication1997
AuthorsBrushe, G. D., V. Krishnamurthy, and L. B. White
JournalCommunications, IEEE Transactions on
Volume45
Pagination1565 -1574
Date Publisheddec.
ISSN0090-6778
Keywordsconvolutional codes, delayed decision-feedback sequence estimation, delays, estimation theory, feedback, fixed reduced-complexity Viterbi algorithm, iterative estimation, iterative methods, model parameters, parameter estimation, performance, recursive expectation maximization, reduced-complexity online parameter estimator, reduced-complexity online state sequence estimator, standard Viterbi algorithm, state estimation, state sequence estimation, superimposed convolutional coded signals, variable reduced-complexity Viterbi algorithm
Abstract

This paper develops a reduced-complexity online state sequence and parameter estimator for superimposed convolutional coded signals. Joint state sequence and parameter estimation is achieved by iteratively estimating the state sequence via a variable reduced-complexity Viterbi algorithm (VRCVA) and the model parameters via a recursive expectation maximization (EM) approach. The VRCVA is developed from a fixed reduced-complexity Viterbi algorithm (FRCVA). The FRCVA is a special case of the delayed decision-feedback sequence estimation (DDFSE) algorithm. The performance of online versions of the FRCVA, VRCVA, and the standard Viterbi algorithm (VA) are compared when they are used to estimate the state sequence as part of the reduced-complexity online state sequence and parameter estimator

URLhttp://dx.doi.org/10.1109/26.650235
DOI10.1109/26.650235

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