Title :
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
fDate :
12/1/1997 12:00:00 AM
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;
Journal_Title :
Communications, IEEE Transactions on
