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

Author

Brushe, Gary D. ; Krishnamurthy, Vikram ; White, Langford B.

Author_Institution

Commun. Div., Defence Sci. & Technol. Organ., Salisbury, SA, Australia

Volume

45

Issue

12

fYear

1997

fDate

12/1/1997 12:00:00 AM

Firstpage

1565

Lastpage

1574

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

Keywords

convolutional codes; delays; estimation theory; feedback; iterative methods; parameter estimation; state estimation; delayed decision-feedback sequence estimation; fixed reduced-complexity Viterbi algorithm; iterative estimation; model parameters; parameter estimation; performance; recursive expectation maximization; reduced-complexity online parameter estimator; reduced-complexity online state sequence estimator; standard Viterbi algorithm; state sequence estimation; superimposed convolutional coded signals; variable reduced-complexity Viterbi algorithm; Brushes; Convolution; Convolutional codes; Delay estimation; Iterative algorithms; Maximum likelihood decoding; Maximum likelihood estimation; Parameter estimation; State estimation; Viterbi algorithm;

fLanguage

English

Journal_Title

Communications, IEEE Transactions on

Publisher

ieee

ISSN

0090-6778

Type

jour

DOI

10.1109/26.650235

Filename

650235