Channel equalization using partial likelihood estimation and recurrent canonical piecewise linear network

A recurrent canonical piecewise linear (RCPL) network is proposed based on the canonical piecewise linear (CPL) structure and is applied to channel equalization. RCPL network provides savings in computation and implementation and has a distinct dynamic behavior completely different than that of finite duration feedforward structure. The simulations of multilevel signal equalization demonstrate the superior performance of RCPL equalizer when compared to the multilayer perceptron equalizer. For the RCPL network, it is easy to incorporate the a-priori information into the network structure. A novel blind algorithm is presented by combining partial likelihood estimation and RCPL structure for the binary communications channel. The simulation results show that RCPL blind equalizer outperforms the CMA equalizer by orders of magnitude for blind equalization of nonlinear communication channels.