3 research outputs found
Efficient clustering techniques for channel equalization in hostile environments
No full textIn 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. (C) 1997 Elsevier Science B.V
