Exact blind channel estimator

Recently blind identification of single-input multiple-output (SIMO) FIR channels has received considerable attention. The obtained exact identification approaches place over-restrictive constraints on the channels. In this thesis least set of constraints on the channels are placed and the noise-free blind channel identification problem is solved in two stages: The identification of the uncommon zeros followed by the identification of the common zeros of the channels. The minimum number of samples required to identify the uncommon zeros is specified, and closed form solutions are obtained. Also a binary-tree algorithm is proposed for the computation of the uncommon zeros efficiently. Then the common zeros of the channels are identified by a novel pruning algorithm. Finally a simulation example is presented to illustrate these ideas.