Blind identification/equalization using deterministic maximum likelihood and a partial prior on the input

Abstract

A (semi)deterministic maximum likelihood (DML) approach is presented to solve the joint blind channel identification and blind symbol estimation problem for single-input multiple-output systems. A partial prior on the symbols is incorporated into the criterion which improves the estimation accuracy and brings robustness toward poor channel diversity conditions. At the same time, this method introduces fewer local minima than the use of a full prior (statistical) ML. In the absence of noise, the proposed batch algorithm estimates perfectly the channel and symbols with a finite number of samples. Based on these considerations, an adaptive implementation of this algorithm is proposed. It presents some desirable properties including low complexity, robustness to channel overestimation, and high convergence rate.