Neural network structures applied for both channel modeling and blind equalization

We propose an approach for blind equalization in digital communication systems. This approach uses a clustering algorithm based on a neural structure. A channel modeling stage is combined to this last approach in order to validate the performance of the equalizer and to guide the choice of its parameters. A neural structure used for the channel modeling task is the modified Elman network. The whole equalization scheme leads to optimal equalization performances especially with linear and nonlinear nonminimum-phase channels