Self-orthogonalizing soft-constraint satisfaction multi-modulus blind equalization algorithms

In this papec a method of accelerating the speed of convergence of a blind equalization algorithm is examined. It is shown that, as in conventional equalizers, the adaptive self-orthogonalization make them appear attractive in blind equalization of a channel. We applied self-orthogonalization to a newly proposed blind equalization scheme [1], [2], known as softconstraint satisfaction multi-modulus algorithm (SCS-MMA-I). We also proposed a stochastic Newtonlike algorithm for SCS-MMA-I. The latter algorithm, which also computes the Hessian of the blind equalization cost function, resulted in better performance.