Identification of fifth-order block-structured nonlinear channels using i.i.d. input signals

This paper is concerned with the problem of nonlinear Wiener channel identification using input-output crossmoments. The static nonlinearity is assumed to be represented by a fifth-degree polynomial. For an i.i.d. input signal, we first derive closed-form expressions for estimating the second-order kernel of the associated fifth-order Volterra model. The parameters of the linear part of the fifth-order Wiener channel are then estimated using an eigenvalue decomposition of the associated second-order Volterra kernel, while the nonlinear subsystem is estimated in the least square sense from the reconstructed output of the linear subsystem. The proposed identification method is illustrated by means of simulation results.