We present Maximum-Likelihood (ML) approaches to semi-blind estimation of multiple FIR channels. The first approach, DML, is based on a deterministic model. The second one, GMLis based on a Gaussian model in which the input sym-bols are considered as Gaussian random variables: this model leads to better and more robust performance than DML. Algo-rithms are presented to solve DML and GML and the significant improvement of GML w.r.t. DML is demonstrated. A soft deci-sion strategy is also presented to improve ML performance: the most reliable decisions taken at the output of an equalizer built from a semi-blind ML channel estimate are treated as known symbols and semi-blind ML is reiterated with an augmented number of known symbols.
Semi-blind maximum-likehood multichannel estimation with gaussian prior for the symbols using soft decisions
VTC 1998, 48th IEEE annual international vehicular technology conference, May 18-21, 1998, Ottawa, Canada
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