Efficient clustering techniques for supervised and blind channel equalization in hostile environments

In this paper the equalization problem is treated as a classification task. No specific (linear or nonlinear) model is required for the channel or for the interference and the noise. Training is achieved via a supervised learning scheme. Adopting Mahalanobis distance as an appropriate distance metric, decisions are made on the basis of minimum distance path. The proposed equalizer operates on a sequence mode and implements the Viterbi searching Algorithm. The robust performance of the equalizer is demonstrated for a hostile environment in the presence of CCI and non linearities, and it is compared against the performance of the MLSE and a symbol by symbol RBF equalizer. Suboptimal techniques with reduced complexity are discussed. The operation of the proposed equalizer in a blind mode is also considered.