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Abstract

An algorithm for computing an efficient, reduced complexity, windowed optimal linear time domain equalizer for a dispersive channel comprises the steps of determining a window of maximum energy in the impulse response of length equal to or less than a number of cyclic prefix samples associated with a received digital data signal, computing the corresponding inside and outside matrices, performing an inverse Cholesky decomposition of the inside matrix, creating a resultant matrix as the product of the outer and the upper and lower square root inner matrix, followed by Householder reduction and QL transformation to thereby compute the time domain equalizer as the linear transformation of the eigenvector corresponding to the smallest eigenvalue at the receiver. The smallest eigenvalue is determined using the aforementioned orthogonal transformations without determining all the eigenvalues efficiently but without the loss accuracy associated with iterative methods like the conventional power method. The algorithm may be most conveniently implemented, for example, in the form of a thirty-two bit digital signal processor at a data receiver.

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