Maximum likelihood joint channel and data estimation using geneticalgorithms

Authors:
S. Chen;Y. Wu
Affiliations:
Dept. of Electr. & Electron. Eng., Portsmouth Univ.;-
Venue:
IEEE Transactions on Signal Processing
Year:
1998

Citing 0
Cited 3

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Robust joint channel and noise estimation in Bayesian blind equalizers

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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