BEHAVIORAL MODELING OF RF POWER AMPLIFIERS USING MODIFIED VOLTERRA SERIES

There has been intensive research in memoryless nonlinear behavioral modeling of power amplifiers (PAs). But in broadband communication systems, memory effects of PAs can no longer be ignored and traditional memoryless model cannot accurately characterize the input-output relationship of PAs. In order to treat memory effects and reduce the complexity of general Volterra model, a new behavioral PA model based on modified Volterra series is proposed. Since the characteristics of power amplifiers change during transmission time, a recursive least squares algorithm with size-fixed observation matrices is developed to update the parameters of the PA model. This identification algorithm, which uses only the latest sample data to identify the parameters, can decrease computational complexity and data storage space needed for identification. Simulations are carried out to validate the performances of the proposed PA model and identification algorithm.