Adaptive Lattice-Hammerstein RLS algorithm for wireless nonlinear equalizers

摘要

In this paper, the adaptive Recursive Least-Squares algorithm is derived for the Lattice-Hammerstein filter leading to the proposed RLS Lattice-Hammerstein nonlinear adaptive filter. The performance of the proposed algorithm is compared to both SG Lattice-Hammerstein and LMS Transversal-Hammerstein algorithms in a nonlinear channel modeling scenario. It is shown that the convergence rate of the RLS Lattice-Hammerstein algorithm is much higher than the other two with a lower steady-state error. Simplicity of the Hammerstein structure compared to the Volterra expression makes is more appropriate for modeling and equalizing nonlinear channels.