Maximum margin equalizers trained with the Adatron algorithm

In this paper we apply the structural risk minimization principle as an appropriate criterion to train decision feedback and transversal equalizers. We consider both linear discriminant (optimal hyperplane) and nonlinear discriminant (support vector machine) classi6ers as an alternative to the linear minimum mean-square error (MMSE) equalizer and radial basis function (RBF) networks, respectively. A fast and simple adaptive algorithm called the Adatron is applied to obtain the linear or nonlinear classi6er. In this way we avoid the high computational cost of quadratic programming. Moreover, the use of soft margin (regularized) classi6ers is proposed as a simple way to consider “noisy” channel states: this alternative improves the bit error rate, mainly at low SNR’s. Furthermore, an adaptive implementation is discussed. Some simulation examples show the advantages of the proposed linear and nonlinear equalizers: a better performance in comparison to the linear MMSE and a simpler structure in comparison to the RBF (Bayesian). ? 2002 Elsevier Science B.V. All rights reserved.