Recurrent canonical piecewise linear network for blind equalization

The recurrent canonical piecewise linear (RCPL) network is applied to nonlinear blind equalization by generalizing Donoho´s minimum entropy deconvolution approach. We first study the approximation ability of the canonical piecewise linear (CPL) network and the CPL based distribution learning for blind equalization. We then generalize these conclusions to the RCPL network. We show that nonlinear blind equalization can be achieved by matching the distribution of the channel input with that of the RCPL equalizer output. A new blind equalizer structure is constructed by using RCPL network and decision feedback. We discuss application of various cost functions to RCPL based equalization and present experimental results that demonstrate the successful application of RCPL network to blind equalization