Blind equalization formulated as a self-organized learning process

A procedure for building a blind equalizer, motivated by neural network theory, is described. The procedure treats the blind equalization problem as a self-organized process. The network consists of an input layer, a single hidden layer, and a single output unit. The learning process proceeds in two stages. In stage I the nonlinear transformation for the input layer to the hidden layer is computed in a self-organized manner, which is frozen once steady-state conditions are reached. Stage II, building on stage I, resembles a conventional Bussgang algorithm except for the fact that the output nonlinearity is adapted alongside the linear weights connected to the output unit.<>