Dynamic Deviation Reduction-Based Volterra Behavioral Modeling of RF Power Amplifiers

Abstract

A new representation of the Volterra series is proposed, which is derived from a previously introduced modified Volterra series, but adapted to the discrete time domain and reformulated in a novel way. Based on this representation, an efficient model-pruning approach, called dynamic deviation reduction, is introduced to simplify the structure of Volterra-series-based RF power amplifier behavioral models aimed at significantly reducing the complexity of the model, but without incurring loss of model fidelity. Both static nonlinearities and different orders of dynamic behavior can be separately identified and the proposed representation retains the important property of linearity with respect to series coefficients. This model can, therefore, be easily extracted directly from the measured time domain of input and output samples of an amplifier by employing simple linear system identification algorithms. A systematic mathematical derivation is presented, together with validation of the proposed method using both computer simulation and experiment