Efficient clustering techniques for channel equalization in hostile environments

Abstract

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 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 nonlinearities, and it is compared against the performance of the MLSE and a symbol by symbol RBF equalizer. Suboptimal techniques with reduced complexity are discussed.