Modeling the nonlinear power amplifier with memory using complex-valued radial basis function networks

In this paper, we propose a novel complex-valued RBF networks approach for dynamic behavioral modeling of nonlinear power amplifier with memory. The complex recursive orthogonal least squares (ROLS) algorithm is applied to train complex radial basis function (RBF) network when modeling, so as to save large memory and computational efforts. Using the information available from the trained network with complex ROLS algorithm, the effective centers of the network can be obtained by adopting backward selection algorithm, which achieve acceptable accuracy with significant reduction of network structure. The model has been validated using measured and simulated data. The results of simulation and experimentation show that this complex RBF network model requires a significantly reduced complexity in the analysis and training procedures, when driven with WCDMA signals, than previously published neural-network-based PA models.