Nonlinear channel equalization using clustering techniques

A new maximum likelihood equalizer is proposed. The equalization task is treated as a nearest neighbor classifier, using appropriately defined measure distances. The clustering procedure is reviewed. The states of the Viterbi algorithm consist of the centers around which the observation points are clustered. Subclustering techniques to reduce complexity are considered and their effect on the performance is evaluated.<
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