Maximum likelihood joint channel and data estimation using genetic algorithms

Author

Chen, S. ; Wu, Y.

Author_Institution

Dept. of Electr. & Electron. Eng., Portsmouth Univ., UK

Volume

46

Issue

5

fYear

1998

fDate

5/1/1998 12:00:00 AM

Firstpage

1469

Lastpage

1473

Abstract

A batch blind equalization scheme is developed based on maximum likelihood joint channel and data estimation. In this scheme, the joint maximum likelihood optimization is decomposed into a two-level optimization loop. A micro genetic algorithm is employed at the upper level to identify the unknown channel model, and the Viterbi algorithm is used at the lower level to provide the maximum likelihood sequence estimation of the transmitted data sequence. As is demonstrated in simulation, the proposed method is much more accurate compared with existing algorithms for joint channel and data estimation

Keywords

adaptive equalisers; genetic algorithms; maximum likelihood estimation; sequences; Viterbi algorithm; adaptive equalisers; batch blind equalization; channel estimation; channel model identification; data estimation; joint estimation; maximum likelihood estimation; maximum likelihood sequence estimation; micro genetic algorithm; simulation; transmitted data sequence; two-level optimization loop; Adaptive algorithm; Blind equalizers; Computational complexity; Convergence; Estimation theory; Genetic algorithms; Iterative decoding; Maximum likelihood decoding; Maximum likelihood estimation; Viterbi algorithm;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.668813

Filename

668813