CMA-based nonlinear blind equaliser modelled by a two-layer feedforward neural network

Combining the `mixture of experts´ (ME) model with the expectation-maximisation (EM) algorithm, the author proposes a constant modulus algorithm-based (CMA-based) blind equalisation scheme for equalising the nonlinear channel. The equaliser considered in the paper is assumed as a two-layer feedforward neural network (FNN). The hidden units of the FNN equaliser are approximated with multiple linear systems around multiple points of its input space, and the FNN equaliser is then remodelled with an ME architecture. Based on the ME model, FNN equaliser parameters are estimated with the statistical EM algorithm in a faster convergence speed than the back-propagating algorithm, in which M-operation of the EM algorithm is in fact the CMA-based linear finite impulse response equalisation