Nonlinear Least Squares Lattice Algorithm for Identifying the Power Amplifier with Memory Effects

The memory polynomial model (MPM) proposed recently, is shown to be a good model to capture the memory nonlinear effects in the power amplifier (PA). When extracting the model coefficients, the memory length of the PA has to be predefined, while it is actually unknown previously. In this paper, the adaptive nonlinear least squares lattice algorithm is employed to identify the PA with memory effects based on MPM. Making use of the order-recursive behavior of the algorithm, the MPM with the optimum memory length is obtained. The computational complexity of the identification algorithm is equivalent to the recursive-least-squares (RLS) algorithm. And the same model accuracy as acquired by the RLS algorithm can be achieved. Simulation results show the fast convergence and numerical stability of the proposed approach