Normalized sliding window constant modulus and decision-directed algorithms: a link between blind equalization and classical adaptive filtering

By minimizing a deterministic criterion of the constant modulus (CM) type or of the decision- directed (DD) type, we derive normalized stochastic gradient algorithms for blind linear equal- ization (BE) of QAM systems. These algorithms allow ustoformulate CM and DD separation principles, which help obtain a whole family of CM or DD BE algorithms from classical adaptive filtering algorithms. We focus on the algorithms obtained by using the Ane Projection adaptive filtering Algorithm (APA). Their increased convergence speed and ability to escape from local minima of their cost function, make these algorithms very promising for BE applications.